Last update:
Sat Nov 8 14:44:26 MST 2025
James Berger and
Donald Estep and
Max Gunzburger Message from the Editors . . . . . . . . 1--1
Desmond J. Higham and
Xuerong Mao and
Mikolaj Roj and
Qingshuo Song and
George Yin Mean Exit Times and the Multilevel Monte
Carlo Method . . . . . . . . . . . . . . 2--18
Art B. Owen Variance Components and Generalized
Sobol' Indices . . . . . . . . . . . . . 19--41
Jerome L. Stein and
Seth Stein Formulating Natural Hazard Policies
under Uncertainty . . . . . . . . . . . 42--56
Victor Picheny and
David Ginsbourger A Nonstationary Space-Time Gaussian
Process Model for Partially Converged
Simulations . . . . . . . . . . . . . . 57--78
Bernard Haasdonk and
Karsten Urban and
Bernhard Wieland Reduced Basis Methods for Parameterized
Partial Differential Equations with
Stochastic Influences Using the
Karhunen--Lo\`eve Expansion . . . . . . 79--105
K. Singh and
A. Sandu and
M. Jardak and
K. W. Bowman and
M. Lee A Practical Method to Estimate
Information Content in the Context of
$4$D-Var Data Assimilation . . . . . . . 106--138
Christian Bender and
Jessica Steiner A Posteriori Estimates for Backward SDEs 139--163
T. Butler and
C. Dawson and
T. Wildey Propagation of Uncertainties Using
Improved Surrogate Models . . . . . . . 164--191
Howard C. Elman and
Qifeng Liao Reduced Basis Collocation Methods for
Partial Differential Equations with
Random Coefficients . . . . . . . . . . 192--217
M. Arnst and
C. Soize and
R. Ghanem Hybrid Sampling/Spectral Method for
Solving Stochastic Coupled Problems . . 218--243
Loic Le Gratiet Bayesian Analysis of Hierarchical
Multifidelity Codes . . . . . . . . . . 244--269
Marta D'Elia and
Max Gunzburger Coarse-Grid Sampling Interpolatory
Methods for Approximating Gaussian
Random Fields . . . . . . . . . . . . . 270--296
Jan Peter Hessling Deterministic Sampling for Propagating
Model Covariance . . . . . . . . . . . . 297--318
K. D. Jarman and
A. M. Tartakovsky A Comparison of Closures for Stochastic
Advection-Diffusion Equations . . . . . 319--347
Zackary R. Kenz and
H. T. Banks and
Ralph C. Smith Comparison of Frequentist and Bayesian
Confidence Analysis Methods on a
Viscoelastic Stenosis Model . . . . . . 348--369
F. Minunno and
M. van Oijen and
D. R. Cameron and
J. S. Pereira Selecting Parameters for Bayesian
Calibration of a Process-Based Model: a
Methodology Based on Canonical
Correlation Analysis . . . . . . . . . . 370--385
Qinian Jin and
Peter Mathé Oracle Inequality for a Statistical
Raus--Gfrerer-Type Rule . . . . . . . . 386--407
Roland Pulch Stochastic Galerkin Methods for
Analyzing Equilibria of Random Dynamical
Systems . . . . . . . . . . . . . . . . 408--430
A. Batou and
C. Soize Calculation of Lagrange Multipliers in
the Construction of Maximum Entropy
Distributions in High Stochastic
Dimension . . . . . . . . . . . . . . . 431--451
Mulin Cheng and
Thomas Y. Hou and
Mike Yan and
Zhiwen Zhang A Data-Driven Stochastic Method for
Elliptic PDEs with Random Coefficients 452--493
Lawrence M. Murray and
Emlyn M. Jones and
John Parslow On Disturbance State-Space Models and
the Particle Marginal
Metropolis--Hastings Sampler . . . . . . 494--521
Thordis L. Thorarinsdottir and
Tilmann Gneiting and
Nadine Gissibl Using Proper Divergence Functions to
Evaluate Climate Models . . . . . . . . 522--534
Matthew J. Heaton and
Tamara A. Greasby and
Stephan R. Sain Modeling Uncertainty in Climate Using
Ensembles of Regional and Global Climate
Models and Multiple Observation-Based
Data Sets . . . . . . . . . . . . . . . 535--559
Daniel Williamson and
Adam T. Blaker Evolving Bayesian Emulators for
Structured Chaotic Time Series, with
Application to Large Climate Models . . 1--28
Dominic Kohler and
Johannes Müller and
Utz Wever Cellular Probabilistic Automata --- A
Novel Method for Uncertainty Propagation 29--54
D. P. Kouri A Multilevel Stochastic Collocation
Algorithm for Optimization of PDEs with
Uncertain Coefficients . . . . . . . . . 55--81
A. Labovsky and
Max Gunzburger An Efficient and Accurate Method for the
Identification of the Most Influential
Random Parameters Appearing in the Input
Data for PDEs . . . . . . . . . . . . . 82--105
Mark Strong and
Jeremy E. Oakley When Is a Model Good Enough? Deriving
the Expected Value of Model Improvement
via Specifying Internal Model
Discrepancies . . . . . . . . . . . . . 106--125
Elaine T. Spiller and
M. J. Bayarri and
James O. Berger and
Eliza S. Calder and
Abani K. Patra and
E. Bruce Pitman and
Robert L. Wolpert Automating Emulator Construction for
Geophysical Hazard Maps . . . . . . . . 126--152
M. Schick and
V. Heuveline and
O. P. Le Ma\^\itre A Newton--Galerkin Method for Fluid Flow
Exhibiting Uncertain Periodic Dynamics 153--173
T. Butler and
D. Estep and
S. Tavener and
C. Dawson and
J. J. Westerink A Measure-Theoretic Computational Method
for Inverse Sensitivity Problems III:
Multiple Quantities of Interest . . . . 174--202
Tan Bui-Thanh and
Omar Ghattas An Analysis of Infinite Dimensional
Bayesian Inverse Shape Acoustic
Scattering and Its Numerical
Approximation . . . . . . . . . . . . . 203--222
Francesca Bonizzoni and
Fabio Nobile Perturbation Analysis for the Darcy
Problem with Log-Normal Permeability . . 223--244
Art B. Owen Sobol' Indices and Shapley Value . . . . 245--251
M. Grigoriu Response Statistics for Random
Heterogeneous Microstructures . . . . . 252--275
Joshua T. Horwood and
Aubrey B. Poore Gauss von Mises Distribution for
Improved Uncertainty Realism in Space
Situational Awareness . . . . . . . . . 276--304
Laura Azzimonti and
Fabio Nobile and
Laura M. Sangalli and
Piercesare Secchi Mixed Finite Elements for Spatial
Regression with PDE Penalization . . . . 305--335
Loic Le Gratiet and
Claire Cannamela and
Bertrand Iooss A Bayesian Approach for Global
Sensitivity Analysis of (Multifidelity)
Computer Codes . . . . . . . . . . . . . 336--363
Peng Chen and
Alfio Quarteroni Weighted Reduced Basis Method for
Stochastic Optimal Control Problems with
Elliptic PDE Constraint . . . . . . . . 364--396
J. Jagalur Mohan and
O. Sahni and
A. Doostan and
A. A. Oberai Variational Multiscale Analysis: The
Fine-Scale Green's Function for
Stochastic Partial Differential
Equations . . . . . . . . . . . . . . . 397--422
Gary Tang and
Gianluca Iaccarino Subsampled Gauss Quadrature Nodes for
Estimating Polynomial Chaos Expansions 423--443
Xueyu Zhu and
Akil Narayan and
Dongbin Xiu Computational Aspects of Stochastic
Collocation with Multifidelity Models 444--463
Nikolas Kantas and
Alexandros Beskos and
Ajay Jasra Sequential Monte Carlo Methods for
High-Dimensional Inverse Problems: a
Case Study for the Navier--Stokes
Equations . . . . . . . . . . . . . . . 464--489
David Ginsbourger and
Jean Baccou and
Clément Chevalier and
Frédéric Perales and
Nicolas Garland and
Yann Monerie Bayesian Adaptive Reconstruction of
Profile Optima and Optimizers . . . . . 490--510
Sergios Agapiou and
Johnathan M. Bardsley and
Omiros Papaspiliopoulos and
Andrew M. Stuart Analysis of the Gibbs Sampler for
Hierarchical Inverse Problems . . . . . 511--544
William Kleiber and
Stephan R. Sain and
Michael J. Wiltberger Model Calibration via Deformation . . . 545--563
Robert B. Gramacy and
Jarad Niemi and
Robin M. Weiss Massively Parallel Approximate Gaussian
Process Regression . . . . . . . . . . . 564--584
A. Pampell and
A. B. Aceves and
G. Srinivasan Predicting Dynamic Trends of the
Atlantic Meridional Overturning
Circulation for Transient and Stochastic
Forcing Effects . . . . . . . . . . . . 585--606
D. O'Malley and
V. V. Vesselinov A Combined Probabilistic\ldots
Nonprobabilistic Decision Analysis for
Contaminant Remediation . . . . . . . . 607--621
Andrew Gordon and
Catherine E. Powell A Preconditioner for Fictitious Domain
Formulations of Elliptic PDEs on
Uncertain Parameterized Domains . . . . 622--646
Nan Chen and
Dimitrios Giannakis and
Radu Herbei and
Andrew J. Majda An MCMC Algorithm for Parameter
Estimation in Signals with Hidden
Intermittent Instability . . . . . . . . 647--669
Sharif Rahman A Generalized ANOVA Dimensional
Decomposition for Dependent Probability
Measures . . . . . . . . . . . . . . . . 670--697
Gongjun Xu and
Guang Lin and
Jingchen Liu Rare-Event Simulation for the Stochastic
Korteweg--de Vries Equation . . . . . . 698--716
Jan Peter Hessling Identification of Complex Models . . . . 717--744
G. Perrin and
C. Soize and
D. Duhamel and
C. Funfschilling A Posteriori Error and Optimal Reduced
Basis for Stochastic Processes Defined
by a Finite Set of Realizations . . . . 745--762
Serge Gratton and
David Titley-Peloquin Stochastic Conditioning of Matrix
Functions . . . . . . . . . . . . . . . 763--783
Feng Bao and
Yanzhao Cao and
Clayton Webster and
Guannan Zhang A Hybrid Sparse-Grid Approach for
Nonlinear Filtering Problems Based on
Adaptive-Domain of the Zakai Equation
Approximations . . . . . . . . . . . . . 784--804
Bertrand Gauthier and
Luc Pronzato Spectral Approximation of the IMSE
Criterion for Optimal Designs in
Kernel-Based Interpolation Models . . . 805--825
Daniel Elfverson and
Donald J. Estep and
Fredrik Hellman and
Axel Målqvist Uncertainty Quantification for
Approximate $p$-Quantiles for Physical
Models with Stochastic Inputs . . . . . 826--850
Evan Kwiatkowski and
Jan Mandel Convergence of the Square Root Ensemble
Kalman Filter in the Large Ensemble
Limit . . . . . . . . . . . . . . . . . 1--17
Rami Atar and
Kenny Chowdhary and
Paul Dupuis Robust Bounds on Risk-Sensitive
Functionals via Rényi Divergence . . . . 18--33
C. Soize Polynomial Chaos Expansion of a
Multimodal Random Vector . . . . . . . . 34--60
Farbod Roosta-Khorasani and
Gábor J. Székely and
Uri M. Ascher Assessing Stochastic Algorithms for
Large Scale Nonlinear Least Squares
Problems Using Extremal Probabilities of
Linear Combinations of Gamma Random
Variables . . . . . . . . . . . . . . . 61--90
Sourabh Banerjee and
Ayanendranath Basu and
Sourabh Bhattacharya and
Smarajit Bose and
Dalia Chakrabarty and
Soumendu Sundar Mukherjee Minimum Distance Estimation of Milky Way
Model Parameters and Related Inference 91--115
Martin Drohmann and
Kevin Carlberg The ROMES Method for Statistical
Modeling of Reduced-Order-Model Error 116--145
Matthias Hwai Yong Tan Sequential Bayesian Polynomial Chaos
Model Selection for Estimation of
Sensitivity Indices . . . . . . . . . . 146--168
T. Arbogast and
D. Estep and
B. Sheehan and
S. Tavener A Posteriori Error Estimates for Mixed
Finite Element and Finite Volume Methods
for Parabolic Problems Coupled through a
Boundary . . . . . . . . . . . . . . . . 169--198
J. Ray and
Z. Hou and
M. Huang and
K. Sargsyan and
L. Swiler Bayesian Calibration of the Community
Land Model Using Surrogates . . . . . . 199--233
Giles Hooker and
Kevin K. Lin and
Bruce Rogers Control Theory and Experimental Design
in Diffusion Processes . . . . . . . . . 234--264
K. D. Jarman and
A. M. Tartakovsky Erratum: A Comparison of Closures for
Stochastic Advection-Diffusion Equations 265--266
Michael B. Giles and
Tigran Nagapetyan and
Klaus Ritter Multilevel Monte Carlo Approximation of
Distribution Functions and Densities . . 267--295
Hans-Werner van Wyk and
Max Gunzburger and
John Burkhardt and
Miroslav Stoyanov Power-Law Noises over General Spatial
Domains and on Nonstandard Meshes . . . 296--319
Si Chen and
Kristofer-Roy G. Reyes and
Maneesh K. Gupta and
Michael C. McAlpine and
Warren B. Powell Optimal Learning in Experimental Design
Using the Knowledge Gradient Policy with
Application to Characterizing
Nanoemulsion Stability . . . . . . . . . 320--345
Liessman Sturlaugson and
John W. Sheppard Sensitivity Analysis of Continuous Time
Bayesian Network Reliability Models . . 346--369
S. Golchi and
D. R. Bingham and
H. Chipman and
D. A. Campbell Monotone Emulation of Computer
Experiments . . . . . . . . . . . . . . 370--392
Daniel Kressner and
Rajesh Kumar and
Fabio Nobile and
Christine Tobler Low-Rank Tensor Approximation for
High-Order Correlation Functions of
Gaussian Random Fields . . . . . . . . . 393--416
H. T. Banks and
Jared Catenacci and
Shuhua Hu Asymptotic Properties of Probability
Measure Estimators in a Nonparametric
Model . . . . . . . . . . . . . . . . . 417--433
J. H. Chaudhry and
D. Estep and
V. Ginting and
S. Tavener A Posteriori Analysis for Iterative
Solvers for Nonautonomous Evolution
Problems . . . . . . . . . . . . . . . . 434--459
Denis Belomestny and
Marcel Ladkau and
John Schoenmakers Multilevel Simulation Based Policy
Iteration for Optimal
Stopping-Convergence and Complexity . . 460--483
Tyrus Berry and
John Harlim Nonparametric Uncertainty Quantification
for Stochastic Gradient Flows . . . . . 484--508
Elisabeth Ullmann and
Catherine E. Powell Solving Log-Transformed Random Diffusion
Problems by Stochastic Galerkin Mixed
Finite Element Methods . . . . . . . . . 509--534
Sebastian J. Vollmer Dimension-Independent MCMC Sampling for
Inverse Problems with Non-Gaussian
Priors . . . . . . . . . . . . . . . . . 535--561
Asif Mahmood and
Robert L. Wolpert and
E. Bruce Pitman A Physics-Based Emulator for the
Simulation of Geophysical Mass Flows . . 562--585
Philip B. Stark Constraints versus Priors . . . . . . . 586--598
B. Staber and
J. Guilleminot Approximate Solutions of Lagrange
Multipliers for Information-Theoretic
Random Field Models . . . . . . . . . . 599--621
Peter Benner and
Akwum Onwunta and
Martin Stoll Low-Rank Solution of Unsteady Diffusion
Equations with Stochastic Coefficients 622--649
Julia Charrier Numerical Analysis of the
Advection-Diffusion of a Solute in
Porous Media with Uncertainty . . . . . 650--685
Jeffrey C. Regier and
Philip B. Stark Mini-Minimax Uncertainty Quantification
for Emulators . . . . . . . . . . . . . 686--708
Mustafa A. Mohamad and
Themistoklis P. Sapsis Probabilistic Description of Extreme
Events in Intermittently Unstable
Dynamical Systems Excited by Correlated
Stochastic Processes . . . . . . . . . . 709--736
Vishwas Rao and
Adrian Sandu A Posteriori Error Estimates for the
Solution of Variational Inverse Problems 737--761
Christian Kuehn Numerical Continuation and SPDE
Stability for the $2$D Cubic-Quintic
Allen--Cahn Equation . . . . . . . . . . 762--789
Xiaobing Feng and
Junshan Lin and
Cody Lorton An Efficient Numerical Method for
Acoustic Wave Scattering in Random Media 790--822
Oliver G. Ernst and
Björn Sprungk and
Hans-Jörg Starkloff Analysis of the Ensemble and Polynomial
Chaos Kalman Filters in Bayesian Inverse
Problems . . . . . . . . . . . . . . . . 823--851
Jonas Ballani and
Lars Grasedyck Hierarchical Tensor Approximation of
Output Quantities of Parameter-Dependent
PDEs . . . . . . . . . . . . . . . . . . 852--872
P. Wang and
D. A. Barajas-Solano and
E. Constantinescu and
S. Abhyankar and
D. Ghosh and
B. F. Smith and
Z. Huang and
A. M. Tartakovsky Probabilistic Density Function Method
for Stochastic ODEs of Power Systems
with Uncertain Power Input . . . . . . . 873--896
M. Chevreuil and
R. Lebrun and
A. Nouy and
P. Rai A Least-Squares Method for Sparse Low
Rank Approximation of Multivariate
Functions . . . . . . . . . . . . . . . 897--921
Elisabeth Ullmann and
Iason Papaioannou Multilevel Estimation of Rare Events . . 922--953
Michael Sinsbeck and
Daniel M. Tartakovsky Impact of Data Assimilation on
Cost-Accuracy Tradeoff in Multifidelity
Models . . . . . . . . . . . . . . . . . 954--968
Pierre Del Moral and
Lawrence M. Murray Sequential Monte Carlo with Highly
Informative Observations . . . . . . . . 969--997
N. Hyvönen and
M. Leinonen Stochastic Galerkin Finite Element
Method with Local Conductivity Basis for
Electrical Impedance Tomography . . . . 998--1019
C. M. Bryant and
S. Prudhomme and
T. Wildey Error Decomposition and Adaptivity for
Response Surface Approximations from
PDEs with Parametric Uncertainty . . . . 1020--1045
A. L. Teckentrup and
P. Jantsch and
C. G. Webster and
M. Gunzburger A Multilevel Stochastic Collocation
Method for Partial Differential
Equations with Random Input Data . . . . 1046--1074
T. J. Dodwell and
C. Ketelsen and
R. Scheichl and
A. L. Teckentrup A Hierarchical Multilevel Markov Chain
Monte Carlo Algorithm with Applications
to Uncertainty Quantification in
Subsurface Flow . . . . . . . . . . . . 1075--1108
Sergey Dolgov and
Boris N. Khoromskij and
Alexander Litvinenko and
Hermann G. Matthies Polynomial Chaos Expansion of Random
Coefficients and the Solution of
Stochastic Partial Differential
Equations in the Tensor Train Format . . 1109--1135
Johnathan M. Bardsley and
Aku Seppänen and
Antti Solonen and
Heikki Haario and
Jari Kaipio Randomize-Then-Optimize for Sampling and
Uncertainty Quantification in Electrical
Impedance Tomography . . . . . . . . . . 1136--1158
Mihály Kovács and
Felix Lindner and
René L. Schilling Weak Convergence of Finite Element
Approximations of Linear Stochastic
Evolution Equations with Additive Lévy
Noise . . . . . . . . . . . . . . . . . 1159--1199
Daniel Sanz-Alonso and
Andrew M. Stuart Long-Time Asymptotics of the Filtering
Distribution for Partially Observed
Chaotic Dynamical Systems . . . . . . . 1200--1220
Xiaojing Wang and
James O. Berger Estimating Shape Constrained Functions
Using Gaussian Processes . . . . . . . . 1--25
S. Montagna and
S. T. Tokdar Computer Emulation with Nonstationary
Gaussian Processes . . . . . . . . . . . 26--47
Josef Dick and
Quoc T. Le Gia and
Christoph Schwab Higher Order Quasi-Monte Carlo
Integration for Holomorphic, Parametric
Operator Equations . . . . . . . . . . . 48--79
Paul Dupuis and
Markos A. Katsoulakis and
Yannis Pantazis and
Petr Plechác Path-Space Information Bounds for
Uncertainty Quantification and
Sensitivity Analysis of Stochastic
Dynamics . . . . . . . . . . . . . . . . 80--111
Lulu Kang and
V. Roshan Joseph Kernel Approximation: From Regression to
Interpolation . . . . . . . . . . . . . 112--129
Sharif Rahman The $f$-Sensitivity Index . . . . . . . 130--162
Bedrich Sousedík and
Howard C. Elman Inverse Subspace Iteration for Spectral
Stochastic Finite Element Methods . . . 163--189
M. Grigoriu Microstructure Models and Material
Response by Extreme Value Theory . . . . 190--217
A. Mittal and
X. Chen and
C. H. Tong and
G. Iaccarino A Flexible Uncertainty Propagation
Framework for General Multiphysics
Systems . . . . . . . . . . . . . . . . 218--243
F. Vidal-Codina and
N. C. Nguyen and
M. B. Giles and
J. Peraire An Empirical Interpolation and
Model-Variance Reduction Method for
Computing Statistical Outputs of
Parametrized Stochastic Partial
Differential Equations . . . . . . . . . 244--265
Mami T. Wentworth and
Ralph C. Smith and
H. T. Banks Parameter Selection and Verification
Techniques Based on Global Sensitivity
Analysis Illustrated for an HIV Model 266--297
David Silvester and
Pranjal An Optimal Solver for Linear Systems
Arising from Stochastic FEM
Approximation of Diffusion Equations
with Random Coefficients . . . . . . . . 298--311
Daniel Elfverson and
Fredrik Hellman and
Axel Målqvist A Multilevel Monte Carlo Method for
Computing Failure Probabilities . . . . 312--330
Jordan Ko and
Henry P. Wynn The Algebraic Method in Quadrature for
Uncertainty Quantification . . . . . . . 331--357
Achref Bachouch and
Emmanuel Gobet and
Anis Matoussi Empirical Regression Method for Backward
Doubly Stochastic Differential Equations 358--379
A. Manzoni and
S. Pagani and
T. Lassila Accurate Solution of Bayesian Inverse
Uncertainty Quantification Problems
Combining Reduced Basis Methods and
Reduction Error Models . . . . . . . . . 380--412
Feng Bao and
Yanzhao Cao and
Amnon Meir and
Weidong Zhao A First Order Scheme for Backward Doubly
Stochastic Differential Equations . . . 413--445
Leonid Berlyand and
Pierre-Emmanuel Jabin and
Mykhailo Potomkin Complexity Reduction in Many Particle
Systems with Random Initial Data . . . . 446--474
David A. Barajas-Solano and
Daniel M. Tartakovsky Stochastic Collocation Methods for
Nonlinear Parabolic Equations with
Random Coefficients . . . . . . . . . . 475--494
Rachel H. Oughton and
Peter S. Craig Hierarchical Emulation: a Method for
Modeling and Comparing Nested Simulators 495--519
Helmut Harbrecht and
Michael Peters and
Markus Siebenmorgen Multilevel Accelerated Quadrature for
PDEs with Log-Normally Distributed
Diffusion Coefficient . . . . . . . . . 520--551
Siddhartha Mishra and
Nils Henrik Risebro and
Christoph Schwab and
Svetlana Tokareva Numerical Solution of Scalar
Conservation Laws with Random Flux
Functions . . . . . . . . . . . . . . . 552--591
Nawinda Chustagulprom and
Sebastian Reich and
Maria Reinhardt A Hybrid Ensemble Transform Particle
Filter for Nonlinear and Spatially
Extended Dynamical Systems . . . . . . . 592--608
H. Cagan Ozen and
Guillaume Bal Dynamical Polynomial Chaos Expansions
and Long Time Evolution of Differential
Equations with Random Forcing . . . . . 609--635
Simon Nanty and
Céline Helbert and
Amandine Marrel and
Nadia Pérot and
Clémentine Prieur Sampling, Metamodeling, and Sensitivity
Analysis of Numerical Simulators with
Functional Stochastic Inputs . . . . . . 636--659
Sonjoy Das and
Sourish Chakravarty Predictive Algorithm for Detection of
Microcracks from Macroscale Observables 660--707
Matthias De Lozzo and
Amandine Marrel Estimation of the Derivative-Based
Global Sensitivity Measures Using a
Gaussian Process Metamodel . . . . . . . 708--738
Joakim Beck and
Serge Guillas Sequential Design with Mutual
Information for Computer Experiments
(MICE): Emulation of a Tsunami Model . . 739--766
Rui Tuo and
C. F. Jeff Wu A Theoretical Framework for Calibration
in Computer Models: Parametrization,
Estimation and Convergence Properties 767--795
Alex Gorodetsky and
Youssef Marzouk Mercer Kernels and Integrated Variance
Experimental Design: Connections Between
Gaussian Process Regression and
Polynomial Approximation . . . . . . . . 796--828
Marcos A. Capistrán and
J. Andrés Christen and
Sophie Donnet Bayesian Analysis of ODEs: Solver
Optimal Accuracy and Bayes Factors . . . 829--849
Dario Azzimonti and
Julien Bect and
Clément Chevalier and
David Ginsbourger Quantifying Uncertainties on Excursion
Sets Under a Gaussian Random Field Prior 850--874
Michael Frenklach and
Andrew Packard and
Gonzalo Garcia-Donato and
Rui Paulo and
Jerome Sacks Comparison of Statistical and
Deterministic Frameworks of Uncertainty
Quantification . . . . . . . . . . . . . 875--901
L. Mark Berliner and
Jenný Brynjarsdóttir A Framework for Multi-Model Ensembling 902--923
E. Bergou and
S. Gratton and
L. N. Vicente Levenberg--Marquardt Methods Based on
Probabilistic Gradient Models and
Inexact Subproblem Solution, with
Application to Data Assimilation . . . . 924--951
Ulrich Römer and
Sebastian Schöps and
Thomas Weiland Stochastic Modeling and Regularity of
the Nonlinear Elliptic curl--curl
Equation . . . . . . . . . . . . . . . . 952--979
Bruno Despres and
Benoit Perthame Uncertainty Propagation; Intrusive
Kinetic Formulations of Scalar
Conservation Laws . . . . . . . . . . . 980--1013
Esperan Padonou and
Olivier Roustant Polar Gaussian Processes and
Experimental Designs in Circular Domains 1014--1033
Angela Kunoth and
Christoph Schwab Sparse Adaptive Tensor Galerkin
Approximations of Stochastic
PDE-Constrained Control Problems . . . . 1034--1059
Eunhye Song and
Barry L. Nelson and
Jeremy Staum Shapley Effects for Global Sensitivity
Analysis: Theory and Computation . . . . 1060--1083
G. Malenova and
M. Motamed and
O. Runborg and
R. Tempone A Sparse Stochastic Collocation
Technique for High-Frequency Wave
Propagation with Uncertainty . . . . . . 1084--1110
D. Galindo and
P. Jantsch and
C. G. Webster and
G. Zhang Accelerating Stochastic Collocation
Methods for Partial Differential
Equations with Random Input Data . . . . 1111--1137
J. Fohring and
E. Haber Adaptive $A$-Optimal Experimental Design
for Linear Dynamical Systems . . . . . . 1138--1159
Matthew Parno and
Tarek Moselhy and
Youssef Marzouk A Multiscale Strategy for Bayesian
Inference Using Transport Maps . . . . . 1160--1190
Colin Fox and
Richard A. Norton Fast Sampling in a Linear-Gaussian
Inverse Problem . . . . . . . . . . . . 1191--1218
Martin Eigel and
Christian Merdon and
Johannes Neumann An Adaptive Multilevel Monte Carlo
Method with Stochastic Bounds for
Quantities of Interest with Uncertain
Data . . . . . . . . . . . . . . . . . . 1219--1245
Caroline Geiersbach and
Clemens Heitzinger and
Gerhard Tulzer Optimal Approximation of the First-Order
Corrector in Multiscale Stochastic
Elliptic PDE . . . . . . . . . . . . . . 1246--1262
Gang Bao and
Chuchu Chen and
Peijun Li Inverse Random Source Scattering
Problems in Several Dimensions . . . . . 1263--1287
Ting Wang and
Muruhan Rathinam Efficiency of the Girsanov
Transformation Approach for Parametric
Sensitivity Analysis of Stochastic
Chemical Kinetics . . . . . . . . . . . 1288--1322
Veronica E. Bowman and
David C. Woods Emulation of Multivariate Simulators
Using Thin-Plate Splines with
Application to Atmospheric Dispersion 1323--1344
András László Convergence and Error Propagation
Results on a Linear Iterative Unfolding
Method . . . . . . . . . . . . . . . . . 1345--1371
Martin Eigel and
Christian Merdon Local Equilibration Error Estimators for
Guaranteed Error Control in Adaptive
Stochastic Higher-Order Galerkin Finite
Element Methods . . . . . . . . . . . . 1372--1397
Jiahua Jiang and
Yanlai Chen and
Akil Narayan A Goal-Oriented Reduced Basis
Methods-Accelerated Generalized
Polynomial Chaos Algorithm . . . . . . . 1398--1420
Peter Binev and
Albert Cohen and
Wolfgang Dahmen and
Ronald DeVore and
Guergana Petrova and
Przemyslaw Wojtaszczyk Data Assimilation in Reduced Modeling 1--29
Panagiotis Tsilifis and
Roger G. Ghanem and
Paris Hajali Efficient Bayesian Experimentation Using
an Expected Information Gain Lower Bound 30--62
Wim van Ackooij and
R. Henrion (Sub-)Gradient Formulae for Probability
Functions of Random Inequality Systems
under Gaussian Distribution . . . . . . 63--87
Alexandre Iolov and
Susanne Ditlevsen and
André Longtin Optimal Design for Estimation in
Diffusion Processes from First Hitting
Times . . . . . . . . . . . . . . . . . 88--110
Michael Griebel and
Christian Rieger Reproducing Kernel Hilbert Spaces for
Parametric Partial Differential
Equations . . . . . . . . . . . . . . . 111--137
Wanting Xu and
Michael L. Stein Maximum Likelihood Estimation for a
Smooth Gaussian Random Field Model . . . 138--175
C. Soize Optimal Partition in Terms of
Independent Random Vectors of Any
Non-Gaussian Vector Defined by a Set of
Realizations . . . . . . . . . . . . . . 176--211
Ruimeng Hu and
Mike Ludkovski Sequential Design for Ranking Response
Surfaces . . . . . . . . . . . . . . . . 212--239
Timothy C. Wallstrom On the Application of McDiarmid's
Inequality to Complex Systems . . . . . 240--245
Hailiang Du and
Leonard A. Smith Rising Above Chaotic Likelihoods . . . . 246--258
Dean S. Oliver Metropolized Randomized Maximum
Likelihood for Improved Sampling from
Multimodal Distributions . . . . . . . . 259--277
Rossana Vermiglio Polynomial Chaos Expansions for the
Stability Analysis of Uncertain Delay
Differential Equations . . . . . . . . . 278--303
Marc Bocquet and
Karthik S. Gurumoorthy and
Amit Apte and
Alberto Carrassi and
Colin Grudzien and
Christopher K. R. T. Jones Degenerate Kalman Filter Error
Covariances and Their Convergence onto
the Unstable Subspace . . . . . . . . . 304--333
Dishi Liu and
Alexander Litvinenko and
Claudia Schillings and
Volker Schulz Quantification of Airfoil
Geometry-Induced Aerodynamic
Uncertainties --- Comparison of
Approaches . . . . . . . . . . . . . . . 334--352
Fayadhoi Ibrahima and
Hamdi A. Tchelepi Multipoint Distribution of Saturation
for Stochastic Nonlinear Two-Phase
Transport . . . . . . . . . . . . . . . 353--377
M. Navarro Jimenez and
O. P. Le Ma\^\itre and
O. M. Knio Nonintrusive Polynomial Chaos Expansions
for Sensitivity Analysis in Stochastic
Differential Equations . . . . . . . . . 378--402
N. E. Owen and
P. Challenor and
P. P. Menon and
S. Bennani Comparison of Surrogate-Based
Uncertainty Quantification Methods for
Computationally Expensive Simulators . . 403--435
Bamdad Hosseini and
Nilima Nigam Well-Posed Bayesian Inverse Problems:
Priors with Exponential Tails . . . . . 436--465
Ahmad Ahmad Ali and
Elisabeth Ullmann and
Michael Hinze Multilevel Monte Carlo Analysis for
Optimal Control of Elliptic PDEs with
Random Coefficients . . . . . . . . . . 466--492
R. Scheichl and
A. M. Stuart and
A. L. Teckentrup Quasi-Monte Carlo and Multilevel Monte
Carlo Methods for Computing Posterior
Expectations in Elliptic Inverse
Problems . . . . . . . . . . . . . . . . 493--518
Chen Su and
Xuemin Tu Sequential Implicit Sampling Methods for
Bayesian Inverse Problems . . . . . . . 519--539
Chu V. Mai and
Bruno Sudret Surrogate Models for Oscillatory Systems
Using Sparse Polynomial Chaos Expansions
and Stochastic Time Warping . . . . . . 540--571
S. Stanhope and
J. E. Rubin and
D. Swigon Robustness of Solutions of the Inverse
Problem for Linear Dynamical Systems
with Uncertain Data . . . . . . . . . . 572--597
Hao Chen and
Jason L. Loeppky and
William J. Welch Flexible Correlation Structure for
Accurate Prediction and Uncertainty
Quantification in Bayesian Gaussian
Process Emulation of a Computer Model 598--620
Qingping Zhou and
Zixi Hu and
Zhewei Yao and
Jinglai Li A Hybrid Adaptive MCMC Algorithm in
Function Spaces . . . . . . . . . . . . 621--639
Michael Sinsbeck and
Wolfgang Nowak Sequential Design of Computer
Experiments for the Solution of Bayesian
Inverse Problems . . . . . . . . . . . . 640--664
Margaret Callahan and
Daniela Calvetti and
Erkki Somersalo Beyond the Model Limit: Parameter
Inference Across Scales . . . . . . . . 665--693
Ioannis Andrianakis and
Nicky McCreesh and
Ian Vernon and
Trevelyan J. McKinley and
Jeremy E. Oakley and
Rebecca N. Nsubuga and
Michael Goldstein and
Richard G. White Efficient History Matching of a High
Dimensional Individual-Based HIV
Transmission Model . . . . . . . . . . . 694--719
A. Abdulle and
G. A. Pavliotis and
U. Vaes Spectral Methods for Multiscale
Stochastic Differential Equations . . . 720--761
Julien Bect and
Ling Li and
Emmanuel Vazquez Bayesian Subset Simulation . . . . . . . 762--786
Xiaoyu Liu and
Serge Guillas Dimension Reduction for Gaussian Process
Emulation: an Application to the
Influence of Bathymetry on Tsunami
Heights . . . . . . . . . . . . . . . . 787--812
Peng Chen and
Alfio Quarteroni and
Gianluigi Rozza Reduced Basis Methods for Uncertainty
Quantification . . . . . . . . . . . . . 813--869
A. Cesmelioglu and
M. Song and
D. Drignei Physics-Based Kriging Surrogates for a
Class of Finite Element Codes . . . . . 870--889
Stefano Pagani and
Andrea Manzoni and
Alfio Quarteroni Efficient State/Parameter Estimation in
Nonlinear Unsteady PDEs by a Reduced
Basis Ensemble Kalman Filter . . . . . . 890--921
M. Grigoriu Estimates of System Response Maxima by
Extreme Value Theory and Surrogate
Models . . . . . . . . . . . . . . . . . 922--955
Jonathan Hobbs and
Amy Braverman and
Noel Cressie and
Robert Granat and
Michael Gunson Simulation-Based Uncertainty
Quantification for Estimating
Atmospheric CO$_2$ from Satellite Data 956--985
Art B. Owen and
Clémentine Prieur On Shapley Value for Measuring
Importance of Dependent Inputs . . . . . 986--1002
Pranay Seshadri and
Akil Narayan and
Sankaran Mahadevan Effectively Subsampled Quadratures for
Least Squares Polynomial Approximations 1003--1023
Bamdad Hosseini Well-Posed Bayesian Inverse Problems
with Infinitely Divisible and
Heavy-Tailed Prior Measures . . . . . . 1024--1060
Noura Fajraoui and
Stefano Marelli and
Bruno Sudret Sequential Design of Experiment for
Sparse Polynomial Chaos Expansions . . . 1061--1085
Malek Ben Salem and
Olivier Roustant and
Fabrice Gamboa and
Lionel Tomaso Universal Prediction Distribution for
Surrogate Models . . . . . . . . . . . . 1086--1109
M. Park and
M. V. Tretyakov Stochastic Resin Transfer Molding
Process . . . . . . . . . . . . . . . . 1110--1135
Yulong Lu and
Andrew Stuart and
Hendrik Weber Gaussian Approximations for Probability
Measures on $ R^d $ . . . . . . . . . . 1136--1165
Alen Alexanderian and
Noemi Petra and
Georg Stadler and
Omar Ghattas Mean-Variance Risk-Averse Optimal
Control of Systems Governed by PDEs with
Random Parameter Fields Using Quadratic
Approximations . . . . . . . . . . . . . 1166--1192
Qin Li and
Li Wang Uniform Regularity for Linear Kinetic
Equations with Random Input Based on
Hypocoercivity . . . . . . . . . . . . . 1193--1219
Andrew Papanicolaou and
Konstantinos Spiliopoulos Dimension Reduction in Statistical
Estimation of Partially Observed
Multiscale Processes . . . . . . . . . . 1220--1247
Craig J. Newsum and
Catherine E. Powell Efficient Reduced Basis Methods for
Saddle Point Problems with Applications
in Groundwater Flow . . . . . . . . . . 1248--1278
Ya-Ting Huang and
James Glimm A Novel Methodology of Stochastic Short
Term Forecasting of Cloud Boundaries . . 1279--1294
N. Bousquet and
T. Klein and
V. Moutoussamy Approximation of Limit State Surfaces in
Monotonic Monte Carlo Settings, with
Applications to Classification . . . . . 1--33
Grace X. Hu and
David R. Kuipers and
Yong Zeng Bayesian Inference via Filtering
Equations for Ultrahigh Frequency Data
(I): Model and Estimation . . . . . . . 34--60
Grace X. Hu and
David R. Kuipers and
Yong Zeng Bayesian Inference via Filtering
Equations for Ultrahigh Frequency Data
(II): Model Selection . . . . . . . . . 61--86
M. D'Elia and
H. C. Edwards and
J. Hu and
E. Phipps and
S. Rajamanickam Ensemble Grouping Strategies for
Embedded Stochastic Collocation Methods
Applied to Anisotropic Diffusion
Problems . . . . . . . . . . . . . . . . 87--117
Donsub Rim and
Scott Moe and
Randall J. LeVeque Transport Reversal for Model Reduction
of Hyperbolic Partial Differential
Equations . . . . . . . . . . . . . . . 118--150
Guillaume Damblin and
Pierre Barbillon and
Merlin Keller and
Alberto Pasanisi and
Éric Parent Adaptive Numerical Designs for the
Calibration of Computer Codes . . . . . 151--179
David A. Barajas-Solano and
Alexandre M. Tartakovsky Probability and Cumulative Density
Function Methods for the Stochastic
Advection--Reaction Equation . . . . . . 180--212
Clemens Heitzinger and
Gudmund Pammer and
Stefan Rigger Cubature Formulas for Multisymmetric
Functions and Applications to Stochastic
Partial Differential Equations . . . . . 213--242
Alex Bespalov and
Leonardo Rocchi Efficient Adaptive Algorithms for
Elliptic PDEs with Random Data . . . . . 243--272
R. Tipireddy and
P. Stinis and
A. M. Tartakovsky Stochastic Basis Adaptation and Spatial
Domain Decomposition for Partial
Differential Equations with Random
Coefficients . . . . . . . . . . . . . . 273--301
Erkan Nane and
Nguyen Huy Tuan Approximate Solutions of Inverse
Problems for Nonlinear Space Fractional
Diffusion Equations with Randomly
Perturbed Data . . . . . . . . . . . . . 302--338
Patrick R. Conrad and
Andrew D. Davis and
Youssef M. Marzouk and
Natesh S. Pillai and
Aaron Smith Parallel Local Approximation MCMC for
Expensive Models . . . . . . . . . . . . 339--373
Kookjin Lee and
Kevin Carlberg and
Howard C. Elman Stochastic Least-Squares
Petrov--Galerkin Method for
Parameterized Linear Systems . . . . . . 374--396
Emanuele Borgonovo and
Max D. Morris and
Elmar Plischke Functional ANOVA with Multiple
Distributions: Implications for the
Sensitivity Analysis of Computer
Experiments . . . . . . . . . . . . . . 397--427
Arun Hegde and
Wenyu Li and
James Oreluk and
Andrew Packard and
Michael Frenklach Consistency Analysis for Massively
Inconsistent Datasets in Bound-to-Bound
Data Collaboration . . . . . . . . . . . 429--456
Rebecca E. Morrison and
Todd A. Oliver and
Robert D. Moser Representing Model Inadequacy: a
Stochastic Operator Approach . . . . . . 457--496
Benjamin Haaland and
Wenjia Wang and
Vaibhav Maheshwari A Framework for Controlling Sources of
Inaccuracy in Gaussian Process Emulation
of Deterministic Computer Experiments 497--521
Fabrice Gamboa and
Thierry Klein and
Agn\`es Lagnoux Sensitivity Analysis Based on Cramér--von
Mises Distance . . . . . . . . . . . . . 522--548
Leifur Thorbergsson and
Giles Hooker Experimental Design for Partially
Observed Markov Decision Processes . . . 549--567
Andrea L. Bertozzi and
Xiyang Luo and
Andrew M. Stuart and
Konstantinos C. Zygalakis Uncertainty Quantification in
Graph-Based Classification of High
Dimensional Data . . . . . . . . . . . . 568--595
Joseph Durante and
Raj Patel and
Warren B. Powell Scenario Generation Methods that
Replicate Crossing Times in Spatially
Distributed Stochastic Systems . . . . . 596--626
Xu He and
Peter Chien On the Instability Issue of
Gradient-Enhanced Gaussian Process
Emulators for Computer Experiments . . . 627--644
Matthew D. Parno and
Youssef M. Marzouk Transport Map Accelerated Markov Chain
Monte Carlo . . . . . . . . . . . . . . 645--682
E. Qian and
B. Peherstorfer and
D. O'Malley and
V. V. Vesselinov and
K. Willcox Multifidelity Monte Carlo Estimation of
Variance and Sensitivity Indices . . . . 683--706
R. N. Gantner and
M. D. Peters Higher-Order Quasi-Monte Carlo for
Bayesian Shape Inversion . . . . . . . . 707--736
Benjamin Peherstorfer and
Boris Kramer and
Karen Willcox Multifidelity Preconditioning of the
Cross-Entropy Method for Rare Event
Simulation and Failure Probability
Estimation . . . . . . . . . . . . . . . 737--761
Alexandros Beskos and
Ajay Jasra and
Kody Law and
Youssef Marzouk and
Yan Zhou Multilevel Sequential Monte Carlo with
Dimension-Independent
Likelihood-Informed Proposals . . . . . 762--786
D. P. Kouri and
T. M. Surowiec Existence and Optimality Conditions for
Risk-Averse PDE-Constrained Optimization 787--815
Sharif Rahman Mathematical Properties of Polynomial
Dimensional Decomposition . . . . . . . 816--844
Jeremie Houssineau and
Adrian N. Bishop Smoothing and Filtering with a Class of
Outer Measures . . . . . . . . . . . . . 845--866
Daniel Sanz-Alonso Importance Sampling and Necessary Sample
Size: an Information Theory Approach . . 867--879
Gal Shulkind and
Lior Horesh and
Haim Avron Experimental Design for Nonparametric
Correction of Misspecified Dynamical
Models . . . . . . . . . . . . . . . . . 880--906
Xun Huan and
Cosmin Safta and
Khachik Sargsyan and
Zachary P. Vane and
Guilhem Lacaze and
Joseph C. Oefelein and
Habib N. Najm Compressive Sensing with
Cross-Validation and Stop-Sampling for
Sparse Polynomial Chaos Expansions . . . 907--936
Assyr Abdulle and
Ibrahim Almuslimani and
Gilles Vilmart Optimal Explicit Stabilized Integrator
of Weak Order 1 for Stiff and Ergodic
Stochastic Differential Equations . . . 937--964
Peter Benner and
Yue Qiu and
Martin Stoll Low-Rank Eigenvector Compression of
Posterior Covariance Matrices for Linear
Gaussian Inverse Problems . . . . . . . 965--989
Sébastien Marmin and
David Ginsbourger and
Jean Baccou and
Jacques Liandrat Warped Gaussian Processes and
Derivative-Based Sequential Designs for
Functions with Heterogeneous Variations 991--1018
Clemens Heitzinger and
Michael Leumüller and
Gudmund Pammer and
Stefan Rigger Existence, Uniqueness, and a Comparison
of Nonintrusive Methods for the
Stochastic Nonlinear Poisson--Boltzmann
Equation . . . . . . . . . . . . . . . . 1019--1042
Gilles Blanchard and
Marc Hoffmann and
Markus Reiß Optimal Adaptation for Early Stopping in
Statistical Inverse Problems . . . . . . 1043--1075
D. Andrew Brown and
Arvind Saibaba and
Sarah Vallélian Low-Rank Independence Samplers in
Hierarchical Bayesian Inverse Problems 1076--1100
Peter Binev and
Albert Cohen and
Olga Mula and
James Nichols Greedy Algorithms for Optimal
Measurements Selection in State
Estimation Using Reduced Models . . . . 1101--1126
Jehanzeb H. Chaudhry and
Nathanial Burch and
Donald Estep Efficient Distribution Estimation and
Uncertainty Quantification for Elliptic
Problems on Domains with Stochastic
Boundaries . . . . . . . . . . . . . . . 1127--1150
Ksenia N. Kyzyurova and
James O. Berger and
Robert L. Wolpert Coupling Computer Models through Linking
Their Statistical Emulators . . . . . . 1151--1171
Rafael Ballester-Ripoll and
Enrique G. Paredes and
Renato Pajarola Tensor Algorithms for Advanced
Sensitivity Metrics . . . . . . . . . . 1172--1197
Nan Chen and
Andrew J. Majda and
Xin T. Tong Rigorous Analysis for Efficient
Statistically Accurate Algorithms for
Solving Fokker--Planck Equations in
Large Dimensions . . . . . . . . . . . . 1198--1223
Andrés F. López-Lopera and
François Bachoc and
Nicolas Durrande and
Olivier Roustant Finite-Dimensional Gaussian
Approximation with Linear Inequality
Constraints . . . . . . . . . . . . . . 1224--1255
S. Krumscheid and
F. Nobile Multilevel Monte Carlo Approximation of
Functions . . . . . . . . . . . . . . . 1256--1293
Dimitris Kamilis and
Nick Polydorides Uncertainty Quantification for
Low-Frequency, Time-Harmonic Maxwell
Equations with Stochastic Conductivity
Models . . . . . . . . . . . . . . . . . 1295--1334
Colin Grudzien and
Alberto Carrassi and
Marc Bocquet Asymptotic Forecast Uncertainty and the
Unstable Subspace in the Presence of
Additive Model Error . . . . . . . . . . 1335--1363
Eric Joseph Hall and
Markos A. Katsoulakis Robust Information Divergences for
Model-Form Uncertainty Arising from
Sparse Data in Random PDE . . . . . . . 1364--1394
Matthias Heinkenschloss and
Boris Kramer and
Timur Takhtaganov and
Karen Willcox Conditional-Value-at-Risk Estimation via
Reduced-Order Models . . . . . . . . . . 1395--1423
Ben Adcock and
Anyi Bao and
John D. Jakeman and
Akil Narayan Compressed Sensing with Sparse
Corruptions: Fault-Tolerant Sparse
Collocation Approximations . . . . . . . 1424--1453
Michael B. Giles and
Francisco Bernal Multilevel Estimation of Expected Exit
Times and Other Functionals of Stopped
Diffusions . . . . . . . . . . . . . . . 1454--1474
Davide Torlo and
Francesco Ballarin and
Gianluigi Rozza Stabilized Weighted Reduced Basis
Methods for Parametrized Advection
Dominated Problems with Random Inputs 1475--1502
Donsub Rim and
Kyle T. Mandli Displacement Interpolation Using
Monotone Rearrangement . . . . . . . . . 1503--1531
Xiu Yang and
Weixuan Li and
Alexandre Tartakovsky Sliced-Inverse-Regression--Aided Rotated
Compressive Sensing Method for
Uncertainty Quantification . . . . . . . 1532--1554
Mengyang Gu and
Long Wang Scaled Gaussian Stochastic Process for
Computer Model Calibration and
Prediction . . . . . . . . . . . . . . . 1555--1583
Max D. Morris Decomposing Functional Model Inputs for
Variance-Based Sensitivity Analysis . . 1584--1599
H. C. Lie and
T. J. Sullivan and
A. L. Teckentrup Random Forward Models and
Log-Likelihoods in Bayesian Inverse
Problems . . . . . . . . . . . . . . . . 1600--1629
M. Croci and
M. B. Giles and
M. E. Rognes and
P. E. Farrell Efficient White Noise Sampling and
Coupling for Multilevel Monte Carlo with
Nonnested Meshes . . . . . . . . . . . . 1630--1655
Ana Djurdjevac and
Charles M. Elliott and
Ralf Kornhuber and
Thomas Ranner Evolving Surface Finite Element Methods
for Random Advection--Diffusion
Equations . . . . . . . . . . . . . . . 1656--1684
Arindam Fadikar and
Dave Higdon and
Jiangzhuo Chen and
Bryan Lewis and
Srinivasan Venkatramanan and
Madhav Marathe Calibrating a Stochastic, Agent-Based
Model Using Quantile-Based Emulation . . 1685--1706
Andrea Barth and
Andreas Stein A Study of Elliptic Partial Differential
Equations with Jump Diffusion
Coefficients . . . . . . . . . . . . . . 1707--1743
Kookjin Lee and
Bedrich Sousedík Inexact Methods for Symmetric Stochastic
Eigenvalue Problems . . . . . . . . . . 1744--1776
Frédéric Cérou and
Bernard Delyon and
Arnaud Guyader and
Mathias Rousset On the Asymptotic Normality of Adaptive
Multilevel Splitting . . . . . . . . . . 1--30
Wanting Xu and
Michael L. Stein and
Ian Wisher Modeling and Predicting Chaotic Circuit
Data . . . . . . . . . . . . . . . . . . 31--52
Minyong R. Lee Modified Active Subspaces Using the
Average of Gradients . . . . . . . . . . 53--66
Joseph L. Hart and
Julie Bessac and
Emil M. Constantinescu Global Sensitivity Analysis for
Statistical Model Parameters . . . . . . 67--92
Gianluca Detommaso and
Tim Dodwell and
Rob Scheichl Continuous Level Monte Carlo and
Sample-Adaptive Model Hierarchies . . . 93--116
Kayla D. Coleman and
Allison Lewis and
Ralph C. Smith and
Brian Williams and
Max Morris and
Bassam Khuwaileh Gradient-Free Construction of Active
Subspaces for Dimension Reduction in
Complex Models with Applications to
Neutronics . . . . . . . . . . . . . . . 117--142
Toshihiro Yamada and
Kenta Yamamoto Second Order Discretization of
Bismut--Elworthy--Li Formula:
Application to Sensitivity Analysis . . 143--173
Andreas Van Barel and
Stefan Vandewalle Robust Optimization of PDEs with Random
Coefficients Using a Multilevel Monte
Carlo Method . . . . . . . . . . . . . . 174--202
Daniel J. Perry and
Robert M. Kirby and
Akil Narayan and
Ross T. Whitaker Allocation Strategies for High Fidelity
Models in the Multifidelity Regime . . . 203--231
Zachary del Rosario and
Minyong Lee and
Gianluca Iaccarino Lurking Variable Detection via
Dimensional Analysis . . . . . . . . . . 232--259
Sergey Dolgov and
Robert Scheichl A Hybrid Alternating Least
Squares--TT-Cross Algorithm for
Parametric PDEs . . . . . . . . . . . . 260--291
Constantin Grigo and
Phaedon-Stelios Koutsourelakis Bayesian Model and Dimension Reduction
for Uncertainty Propagation:
Applications in Random Media . . . . . . 292--323
N. Nüsken and
G. A. Pavliotis Constructing Sampling Schemes via
Coupling: Markov Semigroups and Optimal
Transport . . . . . . . . . . . . . . . 324--382
Andreas Adelmann On Nonintrusive Uncertainty
Quantification and Surrogate Model
Construction in Particle Accelerator
Modeling . . . . . . . . . . . . . . . . 383--416
Adrien Spagnol and
Rodolphe Le Riche and
Sébastien Da Veigao Global Sensitivity Analysis for
Optimization with Variable Selection . . 417--443
Colin Cotter and
Simon Cotter and
Paul Russell Ensemble Transport Adaptive Importance
Sampling . . . . . . . . . . . . . . . . 444--471
Rodrigo Iza-Teran and
Jochen Garcke A Geometrical Method for Low-Dimensional
Representations of Simulations . . . . . 472--496
Michael B. Giles and
Abdul-Lateef Haji-Ali Multilevel Nested Simulation for
Efficient Risk Estimation . . . . . . . 497--525
Christian Kahle and
Kei Fong Lam and
Jonas Latz and
Elisabeth Ullmann Bayesian Parameter Identification in
Cahn--Hilliard Models for Biological
Growth . . . . . . . . . . . . . . . . . 526--552
Rui Tuo Adjustments to Computer Models via
Projected Kernel Calibration . . . . . . 553--578
Benjamin Peherstorfer Multifidelity Monte Carlo Estimation
with Adaptive Low-Fidelity Models . . . 579--603
Dave Osthus and
Scott A. Vander Wiel and
Nelson M. Hoffman and
Frederick J. Wysocki Prediction Uncertainties beyond the
Range of Experience: a Case Study in
Inertial Confinement Fusion Implosion
Experiments . . . . . . . . . . . . . . 604--633
Roland Pulch and
Florian Augustin Stability Preservation in Stochastic
Galerkin Projections of Dynamical
Systems . . . . . . . . . . . . . . . . 634--651
Christian Clason and
Tapio Helin and
Remo Kretschmann and
Petteri Piiroinen Generalized Modes in Bayesian Inverse
Problems . . . . . . . . . . . . . . . . 652--684
David Aristoff Generalizing Parallel Replica Dynamics:
Trajectory Fragments, Asynchronous
Computing, and PDMPs . . . . . . . . . . 685--719
Furong Sun and
Robert B. Gramacy and
Benjamin Haaland and
Earl Lawrence and
Andrew Walker Emulating Satellite Drag from Large
Simulation Experiments . . . . . . . . . 720--759
Nathalie Ayi and
Erwan Faou Analysis of an Asymptotic Preserving
Scheme for Stochastic Linear Kinetic
Equations in the Diffusion Limit . . . . 760--785
Christopher Müller and
Sebastian Ullmann and
Jens Lang A Bramble--Pasciak Conjugate Gradient
Method for Discrete Stokes Equations
with Random Viscosity . . . . . . . . . 787--805
James M. Scott and
M. Paul Laiu and
Cory D. Hauck Analysis of the Zero Relaxation Limit of
Hyperbolic Balance Laws with Random
Initial Data . . . . . . . . . . . . . . 806--837
Ian Vernon and
Samuel E. Jackson and
Jonathan A. Cumming Known Boundary Emulation of Complex
Computer Models . . . . . . . . . . . . 838--876
Matthew J. Zahr and
Kevin T. Carlberg and
Drew P. Kouri An Efficient, Globally Convergent Method
for Optimization Under Uncertainty Using
Adaptive Model Reduction and Sparse
Grids . . . . . . . . . . . . . . . . . 877--912
Lukas Herrmann and
Christoph Schwab and
Jakob Zech Uncertainty Quantification for Spectral
Fractional Diffusion: Sparsity Analysis
of Parametric Solutions . . . . . . . . 913--947
U. Khristenko and
L. Scarabosio and
P. Swierczynski and
E. Ullmann and
B. Wohlmuth Analysis of Boundary Effects on
PDE-Based Sampling of Whittle--Matérn
Random Fields . . . . . . . . . . . . . 948--974
David Swigon and
Shelby R. Stanhope and
Sven Zenker and
Jonathan E. Rubin On the Importance of the Jacobian
Determinant in Parameter Inference for
Random Parameter and Random Measurement
Error Models . . . . . . . . . . . . . . 975--1006
Agnimitra Dasgupta and
Debraj Ghosh Failure Probability Estimation of Linear
Time Varying Systems by Progressive
Refinement of Reduced Order Models . . . 1007--1028
Giovanni Dematteis and
Tobias Grafke and
Eric Vanden-Eijnden Extreme Event Quantification in
Dynamical Systems with Random Components 1029--1059
Giovanni Rabitti and
Emanuele Borgonovo A Shapley--Owen Index for Interaction
Quantification . . . . . . . . . . . . . 1060--1075
Mohammad Motamed Fuzzy-Stochastic Partial Differential
Equations . . . . . . . . . . . . . . . 1076--1104
Arvind K. Saibaba and
Johnathan Bardsley and
D. Andrew Brown and
Alen Alexanderian Efficient Marginalization-Based MCMC
Methods for Hierarchical Bayesian
Inverse Problems . . . . . . . . . . . . 1105--1131
Matthias H. Y. Tan Gaussian Process Modeling of Finite
Element Models with Functional Inputs 1133--1161
M. Gunzburger and
T. Iliescu and
M. Mohebujjaman and
M. Schneier An Evolve-Filter-Relax Stabilized
Reduced Order Stochastic Collocation
Method for the Time-Dependent
Navier--Stokes Equations . . . . . . . . 1162--1184
Bamdad Hosseini Two Metropolis--Hastings Algorithms for
Posterior Measures with Non-Gaussian
Priors in Infinite Dimensions . . . . . 1185--1223
Joseph L. Hart and
Pierre A. Gremaud Robustness of the Sobol' Indices to
Marginal Distribution Uncertainty . . . 1224--1244
Ru Zhang and
C. Devon Lin and
Pritam Ranjan A Sequential Design Approach for
Calibrating Dynamic Computer Simulators 1245--1274
Kookjin Lee and
Howard C. Elman and
Bedrich Sousedík A Low-Rank Solver for the Navier--Stokes
Equations with Uncertain Viscosity . . . 1275--1300
Pranay Seshadri and
Shaowu Yuchi and
Geoffrey T. Parks Dimension Reduction via Gaussian Ridge
Functions . . . . . . . . . . . . . . . 1301--1322
Paul Mycek and
Matthias De Lozzo Multilevel Monte Carlo Covariance
Estimation for the Computation of Sobol'
Indices . . . . . . . . . . . . . . . . 1323--1348
Jaroslav Vondrejc and
Hermann G. Matthies Accurate Computation of Conditional
Expectation for Highly Nonlinear
Problems . . . . . . . . . . . . . . . . 1349--1368
Malek Ben Salem and
François Bachoc and
Olivier Roustant and
Fabrice Gamboa and
Lionel Tomaso Gaussian Process-Based Dimension
Reduction for Goal-Oriented Sequential
Design . . . . . . . . . . . . . . . . . 1369--1397
T. J. Dodwell and
C. Ketelsen and
R. Scheichl and
A. L. Teckentrup ERRATUM: A Hierarchical Multilevel
Markov Chain Monte Carlo Algorithm with
Applications to Uncertainty
Quantification in Subsurface Flow . . . 1398--1399
Victoria Volodina and
Daniel Williamson Diagnostics-Driven Nonstationary
Emulators Using Kernel Mixtures . . . . 1--26
Sharif Rahman A Spline Chaos Expansion . . . . . . . . 27--57
O. R. Pembery and
E. A. Spence The Helmholtz Equation in Random Media:
Well-Posedness and A Priori Bounds . . . 58--87
Marie Kubínová and
Ivana Pultarová Block Preconditioning of Stochastic
Galerkin Problems: New Two-sided
Guaranteed Spectral Bounds . . . . . . . 88--113
Thomas P. Prescott and
Ruth E. Baker Multifidelity Approximate Bayesian
Computation . . . . . . . . . . . . . . 114--138
Laurent van den Bos and
Benjamin Sanderse and
Wim Bierbooms and
Gerard van Bussel Generating Nested Quadrature Rules with
Positive Weights based on Arbitrary
Sample Sets . . . . . . . . . . . . . . 139--169
Emil M. Constantinescu and
Noémi Petra and
Julie Bessac and
Cosmin G. Petra Statistical Treatment of Inverse
Problems Constrained by Differential
Equations-Based Models with Stochastic
Terms . . . . . . . . . . . . . . . . . 170--197
Anthony Fillion and
Marc Bocquet and
Serge Gratton and
Selime Gürol and
Pavel Sakov An Iterative Ensemble Kalman Smoother in
Presence of Additive Model Error . . . . 198--228
Konstantinos Spiliopoulos Information Geometry for Approximate
Bayesian Computation . . . . . . . . . . 229--260
Adi Ditkowski and
Gadi Fibich and
Amir Sagiv Density Estimation in Uncertainty
Propagation Problems Using a Surrogate
Model . . . . . . . . . . . . . . . . . 261--300
Ruben Aylwin and
Carlos Jerez-Hanckes and
Christoph Schwab and
Jakob Zech Domain Uncertainty Quantification in
Computational Electromagnetics . . . . . 301--341
Matteo Giordano and
Hanne Kekkonen Bernstein--von Mises Theorems and
Uncertainty Quantification for Linear
Inverse Problems . . . . . . . . . . . . 342--373
Richard Nickl and
Sara van de Geer and
Sven Wang Convergence Rates for Penalized Least
Squares Estimators in PDE Constrained
Regression Problems . . . . . . . . . . 374--413
Assyr Abdulle and
Andrea Di Blasio A Bayesian Numerical Homogenization
Method for Elliptic Multiscale Inverse
Problems . . . . . . . . . . . . . . . . 414--450
Jonas Latz On the Well-posedness of Bayesian
Inverse Problems . . . . . . . . . . . . 451--482
Dan Crisan and
Alberto López-Yela and
Joaquin Miguez Stable Approximation Schemes for Optimal
Filters . . . . . . . . . . . . . . . . 483--509
P. Héas Selecting Reduced Models in the
Cross-Entropy Method . . . . . . . . . . 511--538
Jeremiah Birrell and
Luc Rey-Bellet Uncertainty Quantification for Markov
Processes via Variational Principles and
Functional Inequalities . . . . . . . . 539--572
Dave Osthus and
Jeffrey D. Hyman and
Satish Karra and
Nishant Panda and
Gowri Srinivasan A Probabilistic Clustering Approach for
Identifying Primary Subnetworks of
Discrete Fracture Networks with
Quantified Uncertainty . . . . . . . . . 573--600
Daniel Schaden and
Elisabeth Ullmann On Multilevel Best Linear Unbiased
Estimators . . . . . . . . . . . . . . . 601--635
Kui Ren and
Sarah Vallélian Characterizing Impacts of Model
Uncertainties in Quantitative
Photoacoustics . . . . . . . . . . . . . 636--667
Matthias Heinkenschloss and
Boris Kramer and
Timur Takhtaganov Adaptive Reduced-Order Model
Construction for Conditional
Value-at-Risk Estimation . . . . . . . . 668--692
Baptiste Broto and
François Bachoc and
Marine Depecker Variance Reduction for Estimation of
Shapley Effects and Adaptation to
Unknown Input Distribution . . . . . . . 693--716
Ying-Chao Hung and
George Michailidis and
Horace PakHai Lok Locating Infinite Discontinuities in
Computer Experiments . . . . . . . . . . 717--747
Jürgen Dölz A Higher Order Perturbation Approach for
Electromagnetic Scattering Problems on
Random Domains . . . . . . . . . . . . . 748--774
Olivier Roustant and
Espéran Padonou and
Yves Deville and
Alo\"\is Clément and
Guillaume Perrin and
Jean Giorla and
Henry Wynn Group Kernels for Gaussian Process
Metamodels with Categorical Inputs . . . 775--806
Gerardo Severino and
Salvatore Cuomo Uncertainty Quantification of Unsteady
Flows Generated by Line-Sources Through
Heterogeneous Geological Formations . . 807--825
Arpan Mukherjee and
Rahul Rai and
Puneet Singla and
Tarunraj Singh and
Abani Patra Overlapping Clustering Based Technique
for Scalable Uncertainty Quantification
in Physical Systems . . . . . . . . . . 827--859
Kevin Bulthuis and
Frank Pattyn and
Maarten Arnst A Multifidelity Quantile-Based Approach
for Confidence Sets of Random Excursion
Sets with Application to Ice-Sheet
Dynamics . . . . . . . . . . . . . . . . 860--890
Matthias H. Y. Tan Bayesian Optimization of Expected
Quadratic Loss for Multiresponse
Computer Experiments with Internal Noise 891--925
Toni Karvonen and
George Wynne and
Filip Tronarp and
Chris Oates and
Simo Särkkä Maximum Likelihood Estimation and
Uncertainty Quantification for Gaussian
Process Approximation of Deterministic
Functions . . . . . . . . . . . . . . . 926--958
Luc Pronzato and
Anatoly Zhigljavsky Bayesian Quadrature, Energy
Minimization, and Space-Filling Design 959--1011
Caroline Moosmüller and
Felix Dietrich and
Ioannis G. Kevrekidis A Geometric Approach to the Transport of
Discontinuous Densities . . . . . . . . 1012--1035
Jeff Borggaard and
Nathan Glatt-Holtz and
Justin Krometis A Bayesian Approach to Estimating
Background Flows from a Passive Scalar 1036--1060
S. Crépey and
G. Fort and
E. Gobet and
U. Stazhynski Uncertainty Quantification for
Stochastic Approximation Limits Using
Chaos Expansion . . . . . . . . . . . . 1061--1089
Amirhossein Taghvaei and
Prashant G. Mehta and
Sean P. Meyn Diffusion Map-based Algorithm for Gain
Function Approximation in the Feedback
Particle Filter . . . . . . . . . . . . 1090--1117
Lun Yang and
Peng Wang and
Daniel M. Tartakovsky Resource-Constrained Model Selection for
Uncertainty Propagation and Data
Assimilation . . . . . . . . . . . . . . 1118--1138
Aaron R. Dinner and
Erik H. Thiede and
Brian Van Koten and
Jonathan Weare Stratification as a General Variance
Reduction Method for Markov Chain Monte
Carlo . . . . . . . . . . . . . . . . . 1139--1188
Martin Eigel and
Manuel Marschall and
Michael Multerer An Adaptive Stochastic Galerkin Tensor
Train Discretization for Randomly
Perturbed Domains . . . . . . . . . . . 1189--1214
Linjie Wen and
Jiangqi Wu and
Linjun Lu and
Jinglai Li A Defensive Marginal Particle Filtering
Method for Data Assimilation . . . . . . 1215--1235
Tomohiko Hironaka and
Michael B. Giles and
Takashi Goda and
Howard Thom Multilevel Monte Carlo Estimation of the
Expected Value of Sample Information . . 1236--1259
Marc Mignolet and
Christian Soize Compressed Principal Component Analysis
of Non-Gaussian Vectors . . . . . . . . 1261--1286
Kellin Rumsey and
Gabriel Huerta and
Justin Brown and
Lauren Hund Dealing with Measurement Uncertainties
as Nuisance Parameters in Bayesian Model
Calibration . . . . . . . . . . . . . . 1287--1309
Aretha L. Teckentrup Convergence of Gaussian Process
Regression with Estimated
Hyper-Parameters and Applications in
Bayesian Inverse Problems . . . . . . . 1310--1337
Yang Yu and
Ning Zhang and
Daniel W. Apley and
Wenxin Jiang Including a Nugget Effect in Lifted
Brownian Covariance Models . . . . . . . 1338--1357
Pulong Ma Objective Bayesian Analysis of a
Cokriging Model for Hierarchical
Multifidelity Codes . . . . . . . . . . 1358--1382
Simon L. Cotter and
Ioannis G. Kevrekidis and
Paul T. Russell Transport Map Accelerated Adaptive
Importance Sampling, and Application to
Inverse Problems Arising from Multiscale
Stochastic Reaction Networks . . . . . . 1383--1413
John Harlim and
Daniel Sanz-Alonso and
Ruiyi Yang Kernel Methods for Bayesian Elliptic
Inverse Problems on Manifolds . . . . . 1414--1445
Colin Cotter and
Dan Crisan and
Darryl D. Holm and
Wei Pan and
Igor Shevchenko A Particle Filter for Stochastic
Advection by Lie Transport: a Case Study
for the Damped and Forced Incompressible
Two-Dimensional Euler Equation . . . . . 1446--1492
Qin Li and
Jian-Guo Liu and
Ruiwen Shu Sensitivity Analysis of Burgers'
Equation with Shocks . . . . . . . . . . 1493--1521
Rui Tuo and
Yan Wang and
C. F. Jeff Wu On the Improved Rates of Convergence for
Matérn-Type Kernel Ridge Regression with
Application to Calibration of Computer
Models . . . . . . . . . . . . . . . . . 1522--1547
Nan Chen An Information Criterion for Choosing
Observation Locations in Data
Assimilation and Prediction . . . . . . 1548--1573
Shuai Lu and
Pingping Niu and
Frank Werner On the Asymptotical Regularization for
Linear Inverse Problems in Presence of
White Noise . . . . . . . . . . . . . . 1--28
Johnathan M. Bardsley and
Tiangang Cui Optimization-Based Markov Chain Monte
Carlo Methods for Nonlinear Hierarchical
Statistical Inverse Problems . . . . . . 29--64
Ujjwal Koley and
Deep Ray and
Tanmay Sarkar Multilevel Monte Carlo Finite Difference
Methods for Fractional Conservation Laws
with Random Data . . . . . . . . . . . . 65--105
M. Ganesh and
Frances Y. Kuo and
Ian H. Sloan Quasi-Monte Carlo Finite Element
Analysis for Wave Propagation in
Heterogeneous Random Media . . . . . . . 106--134
Matthew Dobson and
Yao Li and
Jiayu Zhai Using Coupling Methods to Estimate
Sample Quality of Stochastic
Differential Equations . . . . . . . . . 135--162
Alen Alexanderian and
Noemi Petra and
Georg Stadler and
Isaac Sunseri Optimal Design of Large-scale Bayesian
Linear Inverse Problems Under Reducible
Model Uncertainty: Good to Know What You
Don't Know . . . . . . . . . . . . . . . 163--184
Amine Hadji and
Botond Szabó Can We Trust Bayesian Uncertainty
Quantification from Gaussian Process
Priors with Squared Exponential
Covariance Kernel? . . . . . . . . . . . 185--230
Wenyu Li and
Arun Hegde and
James Oreluk and
Andrew Packard and
Michael Frenklach Representing Model Discrepancy in
Bound-to-Bound Data Collaboration . . . 231--259
Michael Sinsbeck and
Emily Cooke and
Wolfgang Nowak Sequential Design of Computer
Experiments for the Computation of
Bayesian Model Evidence . . . . . . . . 260--279
Amal Ben Abdellah and
Pierre L'Ecuyer and
Art B. Owen and
Florian Puchhammer Density Estimation by Randomized
Quasi-Monte Carlo . . . . . . . . . . . 280--301
Takeru Matsuda and
Yuto Miyatake Estimation of Ordinary Differential
Equation Models with Discretization
Error Quantification . . . . . . . . . . 302--331
Qian Xiao and
Abhyuday Mandal and
C. Devon Lin and
Xinwei Deng EzGP: Easy-to-Interpret Gaussian Process
Models for Computer Experiments with
Both Quantitative and Qualitative
Factors . . . . . . . . . . . . . . . . 333--353
Philipp A. Guth and
Vesa Kaarnioja and
Frances Y. Kuo and
Claudia Schillings and
Ian H. Sloan A Quasi-Monte Carlo Method for Optimal
Control Under Uncertainty . . . . . . . 354--383
Viet Ha Hoang and
Jia Hao Quek and
Christoph Schwab Multilevel Markov Chain Monte Carlo for
Bayesian Inversion of Parabolic Partial
Differential Equations under Gaussian
Prior . . . . . . . . . . . . . . . . . 384--419
Henning Omre and
Kjartan Rimstad Bayesian Spatial Inversion and Conjugate
Selection Gaussian Prior Models . . . . 420--445
Sebastian Reich and
Simon Weissmann Fokker--Planck Particle Systems for
Bayesian Inference: Computational
Approaches . . . . . . . . . . . . . . . 446--482
Wenbo Sun and
Matthew Plumlee and
Jingwen Hu and
Jionghua (Judy) Jin Robust System Design with Limited
Experimental Data and an Inexact
Simulation Model . . . . . . . . . . . . 483--506
Denis Belomestny and
Leonid Iosipoi and
Eric Moulines and
Alexey Naumov and
Sergey Samsonov Variance Reduction for Dependent
Sequences with Applications to
Stochastic Gradient MCMC . . . . . . . . 507--535
Weifeng Zhao and
Juntao Huang and
Wen-An Yong Lattice Boltzmann Method for Stochastic
Convection--Diffusion Equations . . . . 536--563
Antoine Blanchard and
Themistoklis Sapsis Output-Weighted Optimal Sampling for
Bayesian Experimental Design and
Uncertainty Quantification . . . . . . . 564--592
Nora Lüthen and
Stefano Marelli and
Bruno Sudret Sparse Polynomial Chaos Expansions:
Literature Survey and Benchmark . . . . 593--649
Jingwei Hu and
Lorenzo Pareschi and
Yubo Wang Uncertainty Quantification for the BGK
Model of the Boltzmann Equation Using
Multilevel Variance Reduced Monte Carlo
Methods . . . . . . . . . . . . . . . . 650--680
María Magdalena Lucini and
Peter Jan van Leeuwen and
Manuel Pulido Model Error Estimation Using the
Expectation Maximization Algorithm and a
Particle Flow Filter . . . . . . . . . . 681--707
Art B. Owen and
Christopher Hoyt Efficient Estimation of the ANOVA Mean
Dimension, with an Application to Neural
Net Classification . . . . . . . . . . . 708--730
Vishwas Rao and
Mihai Anitescu Efficient Computation of Extreme
Excursion Probabilities for Dynamical
Systems through Rice's Formula . . . . . 731--762
Neil K. Chada and
Jordan Franks and
Ajay Jasra and
Kody J. Law and
Matti Vihola Unbiased Inference for Discretely
Observed Hidden Markov Model Diffusions 763--787
Thomas P. Prescott and
Ruth E. Baker Multifidelity Approximate Bayesian
Computation with Sequential Monte Carlo
Parameter Sampling . . . . . . . . . . . 788--817
Felipe Uribe and
Iason Papaioannou and
Youssef M. Marzouk and
Daniel Straub Cross-Entropy-Based Importance Sampling
with Failure-Informed Dimension
Reduction for Rare Event Simulation . . 818--847
Ana Djurdjevac Linear Parabolic Problems in Random
Moving Domains . . . . . . . . . . . . . 848--879
Jean-Claude Fort and
Thierry Klein and
Agn\`es Lagnoux Global Sensitivity Analysis and
Wasserstein Spaces . . . . . . . . . . . 880--921
Baasansuren Jadamba and
Akhtar A. Khan and
Miguel Sama and
Hans-Jorg Starkloff and
Christiane Tammer A Convex Optimization Framework for the
Inverse Problem of Identifying a Random
Parameter in a Stochastic Partial
Differential Equation . . . . . . . . . 922--952
Daniel Schaden and
Elisabeth Ullmann Asymptotic Analysis of Multilevel Best
Linear Unbiased Estimators . . . . . . . 953--978
Matthieu Martin and
Fabio Nobile PDE-Constrained Optimal Control Problems
with Uncertain Parameters using SAGA . . 979--1012
Zhaopeng Hao and
Zhongqiang Zhang Numerical Approximation of Optimal
Convergence for Fractional Elliptic
Equations with Additive Fractional
Gaussian Noise . . . . . . . . . . . . . 1013--1033
Christopher Drovandi and
David J. Nott and
Daniel E. Pagendam A Semiautomatic Method for History
Matching Using Sequential Monte Carlo 1034--1063
Amy Braverman and
Jonathan Hobbs and
Joaquim Teixeira and
Michael Gunson Post hoc Uncertainty Quantification for
Remote Sensing Observing Systems . . . . 1064--1093
Deanna C. Easley and
Tyrus Berry A Higher Order Unscented Transform . . . 1094--1131
Baptiste Broto and
François Bachoc and
Marine Depecker and
Jean-Marc Martinez Gaussian Linear Approximation for the
Estimation of the Shapley Effects . . . 1132--1151
Valentin Resseguier and
Agustin M. Picard and
Etienne Memin and
Bertrand Chapron Quantifying Truncation-Related
Uncertainties in Unsteady Fluid Dynamics
Reduced Order Models . . . . . . . . . . 1152--1183
Alex Bespalov and
Dirk Praetorius and
Michele Ruggeri Two-Level a Posteriori Error Estimation
for Adaptive Multilevel Stochastic
Galerkin Finite Element Method . . . . . 1184--1216
Wei Fang and
Mike B. Giles Importance Sampling for Pathwise
Sensitivity of Stochastic Chaotic
Systems . . . . . . . . . . . . . . . . 1217--1241
Michal Branicki and
Kenneth Uda Lagrangian Uncertainty Quantification
and Information Inequalities for
Stochastic Flows . . . . . . . . . . . . 1242--1313
Dhruv V. Patel and
Assad A. Oberai GAN-Based Priors for Quantifying
Uncertainty in Supervised Learning . . . 1314--1343
Xujia Zhu and
Bruno Sudret Emulation of Stochastic Simulators Using
Generalized Lambda Models . . . . . . . 1345--1380
Peng Chen and
Omar Ghattas Taylor Approximation for Chance
Constrained Optimization Problems
Governed by Partial Differential
Equations with High-Dimensional Random
Parameters . . . . . . . . . . . . . . . 1381--1410
Elmar Plischke and
Giovanni Rabitti and
Emanuele Borgonovo Computing Shapley Effects for
Sensitivity Analysis . . . . . . . . . . 1411--1437
Mahmood Ettehad and
Simon Foucart Instances of Computational Optimal
Recovery: Dealing with Observation
Errors . . . . . . . . . . . . . . . . . 1438--1456
Panagiota Birmpa and
Markos A. Katsoulakis Uncertainty Quantification for Markov
Random Fields . . . . . . . . . . . . . 1457--1498
Matthew M. Dunlop and
Yunan Yang Stability of Gibbs Posteriors from the
Wasserstein Loss for Bayesian Full
Waveform Inversion . . . . . . . . . . . 1499--1526
Wei Xie and
Cheng Li and
Yuefeng Wu and
Pu Zhang A Nonparametric Bayesian Framework for
Uncertainty Quantification in Stochastic
Simulation . . . . . . . . . . . . . . . 1527--1552
Bangti Jin and
Zehui Zhou and
Jun Zou On the Saturation Phenomenon of
Stochastic Gradient Descent for Linear
Inverse Problems . . . . . . . . . . . . 1553--1588
Holger Dette and
Anatoly A. Zhigljavsky Reproducing Kernel Hilbert Spaces,
Polynomials, and the Classical Moment
Problem . . . . . . . . . . . . . . . . 1589--1614
Deyu Ming and
Serge Guillas Linked Gaussian Process Emulation for
Systems of Computer Models Using Matérn
Kernels and Adaptive Design . . . . . . 1615--1642
Jonathan Cockayne and
Andrew Duncan Probabilistic Gradients for Fast
Calibration of Differential Equation
Models . . . . . . . . . . . . . . . . . 1643--1672
Gildas Mazo A Trade-Off Between Explorations and
Repetitions for Estimators of Two Global
Sensitivity Indices in Stochastic Models
Induced by Probability Measures . . . . 1673--1713
Saumya Bhatnagar and
Won Chang and
Seonjin Kim and
Jiali Wang Computer Model Calibration with Time
Series Data Using Deep Learning and
Quantile Regression . . . . . . . . . . 1--26
John Barr and
Herschel Rabitz A Generalized Kernel Method for Global
Sensitivity Analysis . . . . . . . . . . 27--54
Louis Sharrock and
Nikolas Kantas Joint Online Parameter Estimation and
Optimal Sensor Placement for the
Partially Observed Stochastic
Advection--Diffusion Equation . . . . . 55--95
H\`a Quang Minh Finite Sample Approximations of Exact
and Entropic Wasserstein Distances
Between Covariance Operators and
Gaussian Processes . . . . . . . . . . . 96--124
Devin Francom and
Bruno Sansó and
Ana Kupresanin Landmark-Warped Emulators for Models
with Misaligned Functional Response . . 125--150
Teresa Portone and
Robert D. Moser Bayesian Inference of an Uncertain
Generalized Diffusion Operator . . . . . 151--178
Randolf Altmeyer and
Till Bretschneider and
Josef Janák and
Markus Reiß Parameter Estimation in an SPDE Model
for Cell Repolarization . . . . . . . . 179--199
Michael Giles and
Oliver Sheridan-Methven Analysis of Nested Multilevel Monte
Carlo Using Approximate Normal Random
Variables . . . . . . . . . . . . . . . 200--226
Albert Cohen and
Wolfgang Dahmen and
Olga Mula and
James Nichols Nonlinear Reduced Models for State and
Parameter Estimation . . . . . . . . . . 227--267
Rachel H. Oughton and
Michael Goldstein and
John C. P. Hemmings Intermediate Variable Emulation: Using
Internal Processes in Simulators to
Build More Informative Emulators . . . . 268--293
Hossein Mohammadi and
Peter Challenor and
Daniel Williamson and
Marc Goodfellow Cross-Validation--based Adaptive
Sampling for Gaussian Process Models . . 294--316
Fabian Wagner and
I. Papaioannou and
E. Ullmann The Ensemble Kalman Filter for Rare
Event Estimation . . . . . . . . . . . . 317--349
Amy L. Wilson and
Michael Goldstein and
Chris J. Dent Varying Coefficient Models and Design
Choice for Bayes Linear Emulation of
Complex Computer Models with Limited
Model Evaluations . . . . . . . . . . . 350--378
Baptiste Broto and
François Bachoc and
Laura Clouvel and
Jean-Marc Martinez Block-Diagonal Covariance Estimation and
Application to the Shapley Effects in
Sensitivity Analysis . . . . . . . . . . 379--403
Sharif Rahman and
Ramin Jahanbin A Spline Dimensional Decomposition for
Uncertainty Quantification in High
Dimensions . . . . . . . . . . . . . . . 404--438
Robert Sawko and
Ma\lgorzata J. Zimo\'n Effective Generation of Compressed
Stationary Gaussian Fields . . . . . . . 439--452
Charles-Edouard Bréhier and
David Cohen Strong Rates of Convergence of a
Splitting Scheme for Schrödinger
Equations with Nonlocal Interaction
Cubic Nonlinearity and White Noise
Dispersion . . . . . . . . . . . . . . . 453--480
Jialei Chen and
Zhehui Chen and
Chuck Zhang and
C. F. Jeff Wu APIK: Active Physics-Informed Kriging
Model with Partial Differential
Equations . . . . . . . . . . . . . . . 481--506
El Houcine Bergou and
Youssef Diouane and
Vyacheslav Kungurtsev and
Clément W. Royer A Stochastic Levenberg--Marquardt Method
Using Random Models with Complexity
Results . . . . . . . . . . . . . . . . 507--536
Matthias Katzfuss and
Joseph Guinness and
Earl Lawrence Scaled Vecchia Approximation for Fast
Computer-Model Emulation . . . . . . . . 537--554
Hassan Arbabi and
Themistoklis Sapsis Generative Stochastic Modeling of
Strongly Nonlinear Flows with
Non-Gaussian Statistics . . . . . . . . 555--583
Neil K. Chada and
Ajay Jasra and
Fangyuan Yu Multilevel Ensemble Kalman--Bucy Filters 584--618
Wenjia Wang and
Xiaowei Yue and
Benjamin Haaland and
C. F. Jeff Wu Gaussian Processes with Input Location
Error and Applications to the Composite
Parts Assembly Process . . . . . . . . . 619--650
Josef Dick and
Marcello Longo and
Christoph Schwab Extrapolated Polynomial Lattice Rule
Integration in Computational Uncertainty
Quantification . . . . . . . . . . . . . 651--686
Drew P. Kouri and
John D. Jakeman and
J. Gabriel Huerta Risk-Adapted Optimal Experimental Design 687--716
James M. Salter and
Daniel B. Williamson and
Lauren J. Gregoire and
Tamsin L. Edwards Quantifying Spatio-Temporal Boundary
Condition Uncertainty for the North
American Deglaciation . . . . . . . . . 717--744
Jiahui Zhang and
Anne Gelb and
Theresa Scarnati Empirical Bayesian Inference Using a
Support Informed Prior . . . . . . . . . 745--774
Alexander M. G. Cox and
Simon C. Harris and
Andreas E. Kyprianou and
Minmin Wang Monte Carlo Methods for the Neutron
Transport Equation . . . . . . . . . . . 775--825
Pratik Patil and
Mikael Kuusela and
Jonathan Hobbs Objective Frequentist Uncertainty
Quantification for Atmospheric CO$_2$
Retrievals . . . . . . . . . . . . . . . 827--859
Michael Lindsey and
Jonathan Weare and
Anna Zhang Ensemble Markov Chain Monte Carlo with
Teleporting Walkers . . . . . . . . . . 860--885
Shay Gilpin and
Tomoko Matsuo and
Stephen E. Cohn Continuum Covariance Propagation for
Understanding Variance Loss in Advective
Systems . . . . . . . . . . . . . . . . 886--914
Oliver G. Ernst and
Alois Pichler and
Björn Sprungk Wasserstein Sensitivity of Risk and
Uncertainty Propagation . . . . . . . . 915--948
Peijun Li and
Xu Wang An Inverse Random Source Problem for the
Biharmonic Wave Equation . . . . . . . . 949--974
Laura Scarabosio Deep Neural Network Surrogates for
Nonsmooth Quantities of Interest in
Shape Uncertainty Quantification . . . . 975--1011
Yuchen He and
Namjoon Suh and
Xiaoming Huo and
Sung Ha Kang and
Yajun Mei Asymptotic Theory of $
\ell_1$-Regularized PDE Identification
from a Single Noisy Trajectory . . . . . 1012--1036
Kevin Elie-Dit-Cosaque and
Veronique Maume-Deschamps Goal-Oriented Shapley Effects with
Special Attention to the
Quantile-Oriented Case . . . . . . . . . 1037--1069
Kody J. H. Law and
Vitaly Zankin Sparse Online Variational Bayesian
Regression . . . . . . . . . . . . . . . 1070--1100
Bedrich Sousedík and
Kookjin Lee Stochastic Galerkin Methods for Linear
Stability Analysis of Systems with
Parametric Uncertainty . . . . . . . . . 1101--1129
Hamza Ruzayqat and
Aimad Er-raiy and
Alexandros Beskos and
Dan Crisan and
Ajay Jasra and
Nikolas Kantas A Lagged Particle Filter for Stable
Filtering of Certain High-Dimensional
State-Space Models . . . . . . . . . . . 1130--1161
Paul Hagemann and
Johannes Hertrich and
Gabriele Steidl Stochastic Normalizing Flows for Inverse
Problems: a Markov Chains Viewpoint . . 1162--1190
Paul B. Rohrbach and
Sergey Dolgov and
Lars Grasedyck and
Robert Scheichl Rank Bounds for Approximating Gaussian
Densities in the Tensor-Train Format . . 1191--1224
Victor Churchill and
Anne Gelb Sampling-based Spotlight SAR Image
Reconstruction from Phase History Data
for Speckle Reduction and Uncertainty
Quantification . . . . . . . . . . . . . 1225--1249
Trung Pham and
Alex A. Gorodetsky Ensemble Approximate Control Variate
Estimators: Applications to
MultiFidelity Importance Sampling . . . 1250--1292
Felipe Uribe and
Johnathan M. Bardsley and
Yiqiu Dong and
Per Christian Hansen and
Nicolai A. B. Riis A Hybrid Gibbs Sampler for
Edge-Preserving Tomographic
Reconstruction with Uncertain View
Angles . . . . . . . . . . . . . . . . . 1293--1320
Drew P. Kouri and
Thomas M. Surowiec Corrigendum: Existence and Optimality
Conditions for Risk-Averse
PDE-Constrained Optimization . . . . . . 1321--1322
Daniel Sanz-Alonso and
Ruiyi Yang Finite Element Representations of
Gaussian Processes: Balancing Numerical
and Statistical Accuracy . . . . . . . . 1323--1349
Florian Bourgey and
Emmanuel Gobet and
Clément Rey A Comparative Study of Polynomial-Type
Chaos Expansions for Indicator Functions 1350--1383
Nan Chen and
Quanling Deng and
Samuel Stechmann Superfloe Parameterization with Physics
Constraints for Uncertainty
Quantification of Sea Ice Floes . . . . 1384--1409
Mengqi Hu and
Yifei Lou and
Xiu Yang A General Framework of Rotational Sparse
Approximation in Uncertainty
Quantification . . . . . . . . . . . . . 1410--1434
Mengyang Gu and
Fangzheng Xie and
Long Wang A Theoretical Framework of the Scaled
Gaussian Stochastic Process in
Prediction and Calibration . . . . . . . 1435--1460
Panagiota Birmpa and
Jinchao Feng and
Markos A. Katsoulakis and
Luc Rey-Bellet Model Uncertainty and Correctability for
Directed Graphical Models . . . . . . . 1461--1512
Yifei Wang and
Peng Chen and
Wuchen Li Projected Wasserstein Gradient Descent
for High-Dimensional Bayesian Inference 1513--1532
Shiv Agrawal and
Hwanwoo Kim and
Danie Sanz-Alonso and
Alexander Strang A Variational Inference Approach to
Inverse Problems with Gamma Hyperpriors 1533--1559
Ömer Deniz Akyildiz and
Connor Duffin and
Sotirios Sabanis and
Mark Girolami Statistical Finite Elements via Langevin
Dynamics . . . . . . . . . . . . . . . . 1560--1585
Thierry Klein and
Nicolas Peteilh and
Paul Rochet Test Comparison for Sobol Indices over
Nested Sets of Variables . . . . . . . . 1586--1600
Marcus J. Grote and
Simon Michel and
Fabio Nobile Uncertainty Quantification by Multilevel
Monte Carlo and Local Time-Stepping for
Wave Propagation . . . . . . . . . . . . 1601--1628
Zilong Zou and
Drew P. Kouri and
Wilkins Aquino A Locally Adapted Reduced-Basis Method
for Solving Risk-Averse PDE-Constrained
Optimization Problems . . . . . . . . . 1629--1651
Yan Wang Penalized Projected Kernel Calibration
for Computer Models . . . . . . . . . . 1652--1683
Shiwei Lan and
Shuyi Li and
Babak Shahbaba Scaling Up Bayesian Uncertainty
Quantification for Inverse Problems
Using Deep Neural Networks . . . . . . . 1684--1713
Moyan Li and
Raed Kontar On Negative Transfer and Structure of
Latent Functions in Multioutput Gaussian
Processes . . . . . . . . . . . . . . . 1714--1732
Chih-Li Sung and
Beau David Barber and
Berkley J. Walker Calibration of Inexact Computer Models
with Heteroscedastic Errors . . . . . . 1733--1752
Tobias Jahnke and
Benny Stein A Multilevel Stochastic Collocation
Method for Schrödinger Equations with a
Random Potential . . . . . . . . . . . . 1753--1780
M. B. Lykkegaard and
T. J. Dodwell and
C. Fox and
G. Mingas and
R. Scheichl Multilevel Delayed Acceptance MCMC . . . 1--30
Babak Maboudi Afkham and
Yiqiu Dong and
Per Christian Hansen Uncertainty Quantification of Inclusion
Boundaries in the Context of X-Ray
Tomography . . . . . . . . . . . . . . . 31--61
Wouter Edeling On the Deep Active-Subspace Method . . . 62--90
Juan P. Madrigal-Cianci and
Fabio Nobile and
Raúl Tempone Analysis of a Class of Multilevel Markov
Chain Monte Carlo Algorithms Based on
Independent Metropolis--Hastings . . . . 91--138
Martin Chak and
Nikolas Kantas and
Grigorios A. Pavliotis On the Generalized Langevin Equation for
Simulated Annealing . . . . . . . . . . 139--167
Cédric Travelletti and
David Ginsbourger and
Niklas Linde Uncertainty Quantification and
Experimental Design for Large-Scale
Linear Inverse Problems under Gaussian
Process Priors . . . . . . . . . . . . . 168--198
Christoph Schwab and
Jakob Zech Deep Learning in High Dimension: Neural
Network Expression Rates for Analytic
Functions in $ L^2 (\mathbb {R}^d,
\gamma_d) $ . . . . . . . . . . . . . . 199--234
Keyi Wu and
Peng Chen and
Omar Ghattas A Fast and Scalable Computational
Framework for Large-Scale
High-Dimensional Bayesian Optimal
Experimental Design . . . . . . . . . . 235--261
Jan Glaubitz and
Anne Gelb and
Guohui Song Generalized Sparse Bayesian Learning and
Application to Image Reconstruction . . 262--284
Terrence Alsup and
Benjamin Peherstorfer Context-Aware Surrogate Modeling for
Balancing Approximation and Sampling
Costs in Multifidelity Importance
Sampling and Bayesian Inverse Problems 285--319
Leon Bungert and
Philipp Wacker Complete Deterministic Dynamics and
Spectral Decomposition of the Linear
Ensemble Kalman Inversion . . . . . . . 320--357
Max Ehre and
Rafael Flock and
Martin Fußeder and
Iason Papaioannou and
Daniel Straub Certified Dimension Reduction for
Bayesian Updating with the Cross-Entropy
Method . . . . . . . . . . . . . . . . . 358--388
Adrian N. Bishop and
Pierre Del Moral Robust Kalman and Bayesian Set-Valued
Filtering and Model Validation for
Linear Stochastic Systems . . . . . . . 389--425
Christophette Blanchet-Scalliet and
Bruno Demory and
Thierry Gonon and
Céline Helbert Gaussian Process Regression on Nested
Spaces . . . . . . . . . . . . . . . . . 426--451
Fedor Goncharov and
Éric Barat and
Thomas Dautremer Nonparametric Posterior Learning for
Emission Tomography . . . . . . . . . . 452--479
Maarten V. de Hoop and
Nikola B. Kovachki and
Nicholas H. Nelsen and
Andrew M. Stuart Convergence Rates for Learning Linear
Operators from Noisy Data . . . . . . . 480--513
Baptiste Kerleguer Multifidelity Surrogate Modeling for
Time-Series Outputs . . . . . . . . . . 514--539
Elaine T. Spiller and
Robert L. Wolpert and
Pablo Tierz and
Taylor G. Asher The Zero Problem: Gaussian Process
Emulators for Range-Constrained Computer
Models . . . . . . . . . . . . . . . . . 540--566
E. A. Spence and
J. Wunsch Wavenumber-Explicit Parametric
Holomorphy of Helmholtz Solutions in the
Context of Uncertainty Quantification 567--590
Tim Jahn Noise Level Free Regularization of
General Linear Inverse Problems under
Unconstrained White Noise . . . . . . . 591--615
Jeremy Heng and
Ajay Jasra and
Kody J. H. Law and
Alexander Tarakanov On Unbiased Estimation for Discretized
Models . . . . . . . . . . . . . . . . . 616--645
Ben Mansour Dia A Continuation Method in Bayesian
Inference . . . . . . . . . . . . . . . 646--681
Yian Chen and
Mihai Anitescu Scalable Physics-Based Maximum
Likelihood Estimation Using Hierarchical
Matrices . . . . . . . . . . . . . . . . 682--725
Prerna Patil and
Hessam Babaee Reduced-Order Modeling with
Time-Dependent Bases for PDEs with
Stochastic Boundary Conditions . . . . . 727--756
Claudia Schillings and
Claudia Totzeck and
Philipp Wacker Ensemble-Based Gradient Inference for
Particle Methods in Optimization and
Sampling . . . . . . . . . . . . . . . . 757--787
Shanyin Tong and
Georg Stadler Large Deviation Theory-based Adaptive
Importance Sampling for Rare Events in
High Dimensions . . . . . . . . . . . . 788--813
Elliot Cartee and
Antonio Farah and
April Nellis and
Jacob Van Hook and
Alexander Vladimirsky Quantifying and Managing Uncertainty in
Piecewise-Deterministic Markov Processes 814--847
Cécile Haberstich and
A. Nouy and
G. Perrin Active Learning of Tree Tensor Networks
using Optimal Least Squares . . . . . . 848--876
Christophe Audouze and
Aaron Klein and
Adrian Butscher and
Nigel Morris and
Prasanth Nair and
Masayuki Yano Robust Level-Set-Based Topology
Optimization Under Uncertainties Using
Anchored ANOVA Petrov--Galerkin Method 877--905
Sam Allen and
David Ginsbourger and
Johanna Ziegel Evaluating Forecasts for High-Impact
Events Using Transformed Kernel Scores 906--940
Alexey Chernov and
Erik Marc Schetzke A Simple, Bias-free Approximation of
Covariance Functions by the Multilevel
Monte Carlo Method Having Nearly Optimal
Complexity . . . . . . . . . . . . . . . 941--969
Teo Deveney and
Eike H. Mueller and
Tony Shardlow Deep Surrogate Accelerated
Delayed-Acceptance Hamiltonian Monte
Carlo for Bayesian Inference of
Spatio-Temporal Heat Fluxes in Rotating
Disc Systems . . . . . . . . . . . . . . 970--995
Nathaniel Pritchard and
Vivak Patel Towards Practical Large-Scale Randomized
Iterative Least Squares Solvers through
Uncertainty Quantification . . . . . . . 996--1024
Jasper M. Everink and
Yiqiu Dong and
Martin S. Andersen Bayesian Inference with Projected
Densities . . . . . . . . . . . . . . . 1025--1043
Vladimir Spokoiny Dimension Free Nonasymptotic Bounds on
the Accuracy of High-Dimensional Laplace
Approximation . . . . . . . . . . . . . 1044--1068
Shurui Lv and
Jun Yu and
Yan Wang and
Jiang Du Fast Calibration for Computer Models
with Massive Physical Observations . . . 1069--1104
Remo Kretschmann Are Minimizers of the Onsager--Machlup
Functional Strong Posterior Modes? . . . 1105--1138
Johannes Milz Reliable Error Estimates for Optimal
Control of Linear Elliptic PDEs with
Random Inputs . . . . . . . . . . . . . 1139--1163
Yao Li and
Yaping Yuan Sensitivity Analysis of Quasi-Stationary
Distributions (QSDs) of Mass-Action
Systems . . . . . . . . . . . . . . . . 1164--1194
Hefin Lambley and
T. J. Sullivan An Order-Theoretic Perspective on Modes
and Maximum A Posteriori Estimation in
Bayesian Inverse Problems . . . . . . . 1195--1224
Toni Karvonen Asymptotic Bounds for Smoothness
Parameter Estimates in Gaussian Process
Interpolation . . . . . . . . . . . . . 1225--1257
Vilho Halonen and
Ilkka Pölönen Quantification of Errors Generated by
Uncertain Data in a Linear Boundary
Value Problem Using Neural Networks . . 1258--1277
Yanni Papandreou and
Jon Cockayne and
Mark Girolami and
Andrew Duncan Theoretical Guarantees for the
Statistical Finite Element Method . . . 1278--1307
Sébastien J. Petit and
Julien Bect and
Paul Feliot and
Emmanuel Vazquez Parameter Selection in Gaussian Process
Interpolation: An Empirical Study of
Selection Criteria . . . . . . . . . . . 1308--1328
Seif Ben Bader and
Helmut Harbrecht and
Rolf Krause and
Michael D. Multerer and
Alessio Quaglino and
Marc Schmidlin Space-time Multilevel Quadrature Methods
and their Application for Cardiac
Electrophysiology . . . . . . . . . . . 1329--1356
Suraj Yerramilli and
Akshay Iyer and
Wei Chen and
Daniel W. Apley Fully Bayesian Inference for Latent
Variable Gaussian Process Models . . . . 1357--1381
Zhizhang Wu and
Zhiwen Zhang and
Xiaofei Zhao Error Estimate of a Quasi-Monte Carlo
Time-Splitting Pseudospectral Method for
Nonlinear Schrödinger Equation with
Random Potentials . . . . . . . . . . . 1--29
Daniel Sanz-Alonso and
Nathan Waniorek Analysis of a Computational Framework
for Bayesian Inverse Problems: Ensemble
Kalman Updates and MAP Estimators under
Mesh Refinement . . . . . . . . . . . . 30--68
Jed A. Duersch Projective Integral Updates for
High-Dimensional Variational Inference 69--100
Xianliang Gong and
Yulin Pan Multifidelity Bayesian Experimental
Design to Quantify Rare-Event Statistics 101--127
Guillaume Chennetier and
Hassane Chraibi and
Anne Dutfoy and
Josselin Garnier Adaptive Importance Sampling Based on
Fault Tree Analysis for Piecewise
Deterministic Markov Process . . . . . . 128--156
Chih-Li Sung and
Ji, Yi (Irene) and
Simon Mak and
Wenjia Wang and
Tao Tang Stacking Designs: Designing
Multifidelity Computer Experiments with
Target Predictive Accuracy . . . . . . . 157--181
Ningxin Liu and
Lijian Jiang Perron--Frobenius Operator Filter for
Stochastic Dynamical Systems . . . . . . 182--211
M. Ganesh and
Frances Y. Kuo and
Ian H. Sloan Corrigendum: Quasi--Monte Carlo Finite
Element Analysis for Wave Propagation in
Heterogeneous Random Media . . . . . . . 212--212
Nuojin Cheng and
Osman Asif Malik and
Yiming Xu and
Stephen Becker and
Alireza Doostan and
Akil Narayan Subsampling of Parametric Models with
Bifidelity Boosting . . . . . . . . . . 213--241
Jacob Curran-Sebastian and
Lorenzo Pellis and
Ian Hall and
Thomas House Calculation of Epidemic First Passage
and Peak Time Probability Distributions 242--261
Grigorios A. Pavliotis and
Andrea Zanoni A Method of Moments Estimator for
Interacting Particle Systems and their
Mean Field Limit . . . . . . . . . . . . 262--288
Rafael Ballester-Ripoll Computing Statistical Moments Via
Tensorization of Polynomial Chaos
Expansions . . . . . . . . . . . . . . . 289--308
Carsten Hartmann and
Lorenz Richter Nonasymptotic Bounds for Suboptimal
Importance Sampling . . . . . . . . . . 309--346
Konstantin Häberle and
Barbara Bravi and
Anthea Monod Wavelet-Based Density Estimation for
Persistent Homology . . . . . . . . . . 347--376
Fabienne Comte and
Valentine Genon-Catalot Nonparametric Estimation for Independent
and Identically Distributed Stochastic
Differential Equations with Space-Time
Dependent Coefficients . . . . . . . . . 377--410
Omar Al-Ghattas and
Jiajun Bao and
Daniel Sanz-Alonso Ensemble Kalman Filters with Resampling 411--441
Jan Glaubitz and
Anne Gelb Leveraging Joint Sparsity in
Hierarchical Bayesian Learning . . . . . 442--472
Yi Ji and
Henry Shaowu Yuchi and
Derek Soeder and
J.-F. Paquet and
Steffen A. Bass and
V. Roshan Joseph and
C. F. Jeff Wu and
Simon Mak Conglomerate Multi-fidelity Gaussian
Process Modeling, with Application to
Heavy-Ion Collisions . . . . . . . . . . 473--502
Helmut Harbrecht and
Viacheslav Karnaev and
Marc Schmidlin Quantifying Domain Uncertainty in Linear
Elasticity . . . . . . . . . . . . . . . 503--523
D. Calvetti and
E. Somersalo Computationally Efficient Sampling
Methods for Sparsity Promoting
Hierarchical Bayesian Models . . . . . . 524--548
Qin Li and
Li Wang and
Yunan Yang Differential Equation-Constrained
Optimization with Stochasticity . . . . 524--548
Jianbo Cui and
Liying Sun Quantifying the Effect of Random
Dispersion for Logarithmic Schrödinger
Equation . . . . . . . . . . . . . . . . 579--613
Philipp A. Guth and
Claudia Schillings and
Simon Weissmann One-Shot Learning of Surrogates in
PDE-Constrained Optimization under
Uncertainty . . . . . . . . . . . . . . 614--645
Kellin N. Rumsey and
Devin Francom and
Andy Shen Generalized Bayesian MARS: Tools for
Stochastic Computer Model Emulation . . 646--666
Margot Herin and
Marouane Il Idrissi and
Vincent Chabridon and
Bertrand Iooss Proportional Marginal Effects for Global
Sensitivity Analysis . . . . . . . . . . 667--692
Fabio Nobile and
Tommaso Vanzan A Combination Technique for Optimal
Control Problems Constrained by Random
PDEs . . . . . . . . . . . . . . . . . . 693--721
Nada Cvetkovi\'c and
Han Cheng Lie and
Harshit Bansal and
Karen Veroy Choosing Observation Operators to
Mitigate Model Error in Bayesian Inverse
Problems . . . . . . . . . . . . . . . . 723--758
Feng Yu and
Lixin Shen and
Guohui Song Hyperparameter Estimation for Sparse
Bayesian Learning Models . . . . . . . . 759--787
Hossein Mohammadi and
Peter Challenor and
Marc Goodfellow Emulating Complex Dynamical Simulators
with Random Fourier Features . . . . . . 788--811
Liu Liu and
Kunlun Qi Spectral Convergence of a
Semi-discretized Numerical System for
the Spatially Homogeneous Boltzmann
Equation with Uncertainties . . . . . . 812--841
Mircea Grigoriu Harmonizable Nonstationary Processes . . 842--867
Ricardo Baptista and
Bamdad Hosseini and
Nikola B. Kovachki and
Youssef M. Marzouk Conditional Sampling with Monotone GANs:
From Generative Models to
Likelihood-Free Inference . . . . . . . 868--900
Niklas Baumgarten and
Sebastian Krumscheid and
Christian Wieners A Fully Parallelized and Budgeted
Multilevel Monte Carlo Method and the
Application to Acoustic Waves . . . . . 901--931
D. Elfverson and
R. Scheichl and
S. Weissmann and
F. A. Diaz De La O Adaptive Multilevel Subset Simulation
with Selective Refinement . . . . . . . 932--963
Zhaohui Li and
Shihao Yang and
C. F. Jeff Wu Parameter Inference Based on Gaussian
Processes Informed by Nonlinear Partial
Differential Equations . . . . . . . . . 964--1004
Thomas O. Dixon and
James E. Warner and
Geoffrey F. Bomarito and
Alex A. Gorodetsky Covariance Expressions for Multifidelity
Sampling with Multioutput,
Multistatistic Estimators: Application
to Approximate Control Variates . . . . 1005--1049
Aksel K. Rasmussen and
Fanny Seizilles and
Mark Girolami and
Ieva Kazlauskaite The Bayesian Approach to Inverse Robin
Problems . . . . . . . . . . . . . . . . 1050--1084
Tao Tang and
Simon Mak and
David Dunson Hierarchical Shrinkage Gaussian
Processes: Applications to Computer Code
Emulation and Dynamical System Recovery 1085--1112
Xu He Efficient Kriging Using Interleaved
Lattice-Based Designs with Low Fill and
High Separation Distance Properties . . 1113--1134
Étienne de Montbrun and
Sébastien Gerchinovitz Certified Multifidelity Zeroth-Order
Optimization . . . . . . . . . . . . . . 1135--1164
Zongren Zou and
Tingwei Meng and
Paula Chen and
Jérôme Darbon and
George Em Karniadakis Leveraging Viscous Hamilton--Jacobi PDEs
for Uncertainty Quantification in
Scientific Machine Learning . . . . . . 1165--1191
Hengrui Luo and
Justin D. Strait Multiple Closed Curve Modeling with
Uncertainty Quantification for Shape
Analysis . . . . . . . . . . . . . . . . 1192--1212
Luc Pronzato and
Maria-João Rendas Weighted Leave-One-Out Cross Validation 1213--1239
Sören Christensen and
Asbjòrn Holk Thomsen and
Lukas Trottner Data-Driven Rules for Multidimensional
Reflection Problems . . . . . . . . . . 1240--1272
Carlo Graziani and
Marieme Ngom Targeted Adaptive Design . . . . . . . . 1273--1314
Kota Takeda and
Takashi Sakajo Uniform Error Bounds of the Ensemble
Transform Kalman Filter for Chaotic
Dynamics with Multiplicative Covariance
Inflation . . . . . . . . . . . . . . . 1315--1335
V. Roshan Joseph and
William E. Lewis and
Henry S. Yuchi and
Kathryn A. Maupin Discovering the Unknowns: a First Step 1336--1348
Ruijian Han and
Boris Kramer and
Dongjin Lee and
Akil Narayan and
Yiming Xu An Approximate Control Variates Approach
to Multifidelity Distribution Estimation 1349--1388
Zhiwei Gao and
Liang Yan and
Tao Zhou Adaptive Operator Learning for
Infinite-Dimensional Bayesian Inverse
Problems . . . . . . . . . . . . . . . . 1389--1423
Hui Xu and
Mircea Grigoriu Finite-Dimensional Models for Response
Analysis . . . . . . . . . . . . . . . . 1424--1449
Joy N. Mueller and
Khachik Sargsyan and
Craig J. Daniels and
Habib N. Najm Polynomial Chaos Surrogate Construction
for Random Fields with Parametric
Uncertainty . . . . . . . . . . . . . . 1--29
Minji Kim and
Kevin O'Connor and
Vladas Pipiras and
Themistoklis Sapsis Sampling Low-Fidelity Outputs for
Estimation of High-Fidelity Density and
Its Tails . . . . . . . . . . . . . . . 30--62
Philip Greengard and
Manas Rachh and
Alex H. Barnett Equispaced Fourier Representations for
Efficient Gaussian Process Regression
from a Billion Data Points . . . . . . . 63--89
Felix Terhag and
Philipp Knechtges and
Achim Basermann and
Raúl Tempone Uncertainty Quantification in Machine
Learning Based Segmentation: a Post-Hoc
Approach for Left Ventricle Volume
Estimation in MRI . . . . . . . . . . . 90--113
Vinh Hoang and
Luis Espath and
Sebastian Krumscheid and
Raúl Tempone Scalable Method for Bayesian
Experimental Design without Integrating
over Posterior Distribution . . . . . . 114--139
Francesco A. B. Silva and
Cecilia Pagliantini and
Karen Veroy An Adaptive Hierarchical Ensemble Kalman
Filter with Reduced Basis Models . . . . 140--170
Simon Foucart and
Nicolas Hengartner Worst-Case Learning under a
Multifidelity Model . . . . . . . . . . 171--194
Devin Francom and
J. Derek Tucker and
Gabriel Huerta and
Kurtis Shuler and
Daniel Ries Elastic Bayesian Model Calibration . . . 195--227
Daria Semochkina and
Alexander I. J. Forrester and
David C. Woods Multiobjective Optimization Using
Expected Quantile Improvement for
Decision Making in Disease Outbreaks . . 228--250
Didier Chauveau and
Pierre Vandekerkhove Entropy-Based Burn-in Time Analysis and
Ranking for (A)MCMC Algorithms in High
Dimension . . . . . . . . . . . . . . . 251--277
Nicola\"\i Gouraud and
Pierre Le Bris and
Adrien Majka and
Pierre Monmarché HMC and Underdamped Langevin United in
the Unadjusted Convex Smooth Case . . . 278--303
Bamdad Hosseini and
Alexander W. Hsu and
Amirhossein Taghvaei Conditional Optimal Transport on
Function Spaces . . . . . . . . . . . . 304--338
Jake J. Harmon and
Svetlana Tokareva and
Anatoly Zlotnik and
Pieter J. Swart Adaptive Uncertainty Quantification for
Stochastic Hyperbolic Conservation Laws 339--374
P. Vanmechelen and
G. Lombaert and
G. Samaey Multilevel Markov Chain Monte Carlo with
Likelihood Scaling for Bayesian
Inversion with High-resolution
Observations . . . . . . . . . . . . . . 375--399
Lea Friedli and
David Ginsbourger and
Arnaud Doucet and
Niklas Linde An Energy-Based Model Approach to Rare
Event Probability Estimation . . . . . . 400--424
Denis Belomestny and
Tatiana Orlova Statistical Inference for Conservation
Law McKean--Vlasov SDEs via Deep Neural
Networks . . . . . . . . . . . . . . . . 425--448
Chih-Li Sung and
Yao Song and
Ying Hung Advancing Inverse Scattering with
Surrogate Modeling and Bayesian
Inference for Functional Inputs . . . . 449--471
Xin An and
Josef Dick and
Michael Feischl and
Andrea Scaglioni and
Thanh Tran Sparse Grid Approximation of Nonlinear
SPDEs: The Landau--Lifshitz--Gilbert
Equation . . . . . . . . . . . . . . . . 472--517
Xiaoli Feng and
Qiang Yao and
Peijun Li and
Xu Wang An Inverse Source Problem for the
Stochastic Multiterm Time-Fractional
Diffusion-Wave Equation . . . . . . . . 518--542
Mikhail Tsitsvero and
Mingoo Jin and
Andrey Lyalin Learning Inducing Points and Uncertainty
on Molecular Data by Scalable
Variational Gaussian Processes . . . . . 543--562
Massimo Aufiero and
Lucas Janson Surrogate-Based Global Sensitivity
Analysis with Statistical Guarantees via
Floodgate . . . . . . . . . . . . . . . 563--590
Wenzhe Xu and
Daniel B. Williamson and
Frederic Hourdin and
Romain Roehrig Feature Calibration for Computer Models 591--612
Alex Bespalov and
Dirk Praetorius and
Thomas Round and
Andrey Savinov Goal-Oriented Error Estimation and
Adaptivity for Stochastic Collocation
FEM . . . . . . . . . . . . . . . . . . 613--638
Tan Zhang and
Zhongjian Wang and
Jack Xin and
Zhiwen Zhang A Convergent Interacting Particle Method
for Computing KPP Front Speeds in Random
Flows . . . . . . . . . . . . . . . . . 639--678
Masha Naslidnyk and
Motonobu Kanagawa and
Toni Karvonen and
Maren Mahsereci Comparing Scale Parameter Estimators for
Gaussian Process Interpolation with the
Brownian Motion Prior: Leave-One-Out
Cross Validation and Maximum Likelihood 679--717
Caroline Tatsuoka and
Dongbin Xiu Deep Learning for Model Correction of
Dynamical Systems with Data Scarcity . . 718--743
Ahmed Attia and
Sven Leyffer and
Todd S. Munson Robust A-Optimal Experimental Design for
Sensor Placement in Bayesian Linear
Inverse Problems . . . . . . . . . . . . 744--774
Fuqun Han and
Stanley Osher and
Wuchen Li Tensor Train Based Sampling Algorithms
for Approximating Regularized
Wasserstein Proximal Operators . . . . . 775--804
Yuga Iguchi and
Ajay Jasra and
Mohamed Maama and
Alexandros Beskos Antithetic Multilevel Methods for
Elliptic and Hypoelliptic Diffusions
with Applications . . . . . . . . . . . 805--830
Dylan Green and
Jonathan Lindbloom and
Anne Gelb Complex-Valued Signal Recovery Using a
Generalized Bayesian LASSO . . . . . . . 831--861
Ziyu Chen and
Markos A. Katsoulakis and
Luc Rey-Bellet and
Wei Zhu Statistical Guarantees of
Group-Invariant GANs . . . . . . . . . . 862--890
Sebastian W. Ertel On the Mean Field Theory of Ensemble
Kalman Filters for SPDEs . . . . . . . . 891--930
Marc Dambrine and
Giulio Gargantini and
Helmut Harbrecht and
Jérôme Maynadier Shape Optimization under Constraints on
the Probability of a Quadratic
Functional to Exceed a Given Threshold 931--956
Marc Hoffmann and
Camille Pouchol Regularization for the Approximation of
Functions by Mollified Discretization
Methods . . . . . . . . . . . . . . . . 957--979
Jingtao Zhang and
Xi Chen Multilevel Monte Carlo Metamodeling for
Variance Function Estimation . . . . . . 980--1027
René Henrion and
Georg Stadler and
Florian Wechsung Optimal Control under Uncertainty with
Joint Chance State Constraints:
Almost-Everywhere Bounds, Variance
Reduction, and Application to (Bi)linear
Elliptic PDEs . . . . . . . . . . . . . 1028--1053
Hwanwoo Kim and
Daniel Sanz-Alonso Enhancing Gaussian Process Surrogates
for Optimization and Posterior
Approximation via Random Exploration . . 1054--1084
Jiaheng Chen and
Daniel Sanz-Alonso Precision and Cholesky Factor Estimation
for Gaussian Processes . . . . . . . . . 1085--1115
Pu-Zhao Kow and
Jenn-Nan Wang Consistency of Bayesian Inference for a
Subdiffusion Equation . . . . . . . . . 1116--1144
Laurence Grammont and
François Bachoc and
Andrés F. López-Lopera Error Bounds for a Kernel-Based
Constrained Optimal Smoothing
Approximation . . . . . . . . . . . . . 1145--1173
Jiarui Du and
Zhijian He Unbiased Markov Chain Quasi-Monte Carlo
for Gibbs Samplers . . . . . . . . . . . 1174--1199
Alex Glyn-Davies and
Connor Duffin and
Ieva Kazlauskaite and
Mark Girolami and
Ö. Deniz Akyildiz Statistical Finite Elements via
Interacting Particle Langevin Dynamics 1200--1227
Philipp A. Guth and
Peter Kritzer and
Karl Kunisch Quasi-Monte Carlo Integration for
Feedback Control Under Uncertainty . . . 1228--1264
Lezhi Tan and
Jianfeng Lu Accelerate Langevin Sampling with
Birth--Death Process and Exploration
Component . . . . . . . . . . . . . . . 1265--1293
Arnulf Jentzen and
Adrian Riekert Non-convergence to Global Minimizers for
Adam and Stochastic Gradient Descent
Optimization and Constructions of Local
Minimizers in the Training of Artificial
Neural Networks . . . . . . . . . . . . 1294--1333
Bangti Jin and
Qimeng Quan and
Wenlong Zhang Stochastic Convergence Analysis of the
Inverse Potential Problem . . . . . . . 1334--1373
Chris Chi and
Jonathan Weare and
Aaron R. Dinner Sampling Parameters of Ordinary
Differential Equations with Constrained
Langevin Dynamics . . . . . . . . . . . 1374--1405
Promit Chakroborty and
Somayajulu L. N. Dhulipala and
Michael D. Shields Covariance-Free Bifidelity Control
Variates Importance Sampling for Rare
Event Reliability Analysis . . . . . . . 1406--1451
Kim Batselier Low-dimensional Subspace Regularization
through Structured Tensor Priors . . . . 1452--1474
Rebekah D. White and
John D. Jakeman and
Tim Wildey and
Troy Butler Building Population-Informed Priors for
Bayesian Inference Using Data-Consistent
Stochastic Inversion . . . . . . . . . . 1475--1500
Jonathan Owen and
Ian Vernon Bayesian Emulation of Grey-Box
Multimodel Ensembles Exploiting Known
Interior Structure . . . . . . . . . . . 1501--1542
Zhiwei Gao and
George Em Karniadakis Scalable Bayesian Physics-Informed
Kolmogorov--Arnold Networks . . . . . . 1543--1577
Joonha Park Scalable Simulation-Based Inference for
Implicitly Defined Models Using a
Metamodel for Monte Carlo Log-Likelihood
Estimator . . . . . . . . . . . . . . . 1578--1615
Justin D. Strait and
Kelly R. Moran and
Alexander C. Murph and
Jeffrey D. Hyman and
Hari S. Viswanathan and
Philip H. Stauffer Covariate-Informed Bifidelity Bias
Correction of Distributional Output . . 1616--1648
Stephen Huan and
Joseph Guinness and
Matthias Katzfuss and
Houman Owhadi and
Florian Schäofer Sparse Inverse Cholesky Factorization of
Dense Kernel Matrices by Greedy
Conditional Selection . . . . . . . . . 1649--1679
Yuan Gao and
Di Qi Mean Field Games for Controlling
Coherent Structures in Nonlinear Fluid
Systems . . . . . . . . . . . . . . . . 1681--1708
P. Michael Kielstra and
Michael Lindsey A Gradient-based and Determinant-free
Framework for Fully Bayesian Gaussian
Process Regression . . . . . . . . . . . 1709--1734
Xinzhe Zuo and
Stanley Osher and
Wuchen Li Gradient-Adjusted Underdamped Langevin
Dynamics for Sampling . . . . . . . . . 1735--1765
Anita Shahrokhian and
Xinwei Deng and
C. Devon Lin and
Pritam Ranjan and
Li Xu Adaptive Design for Contour Estimation
from Computer Experiments with
Quantitative and Qualitative Inputs . . 1766--1790
Wenlong Li and
Jian-Feng Yang and
Peter Chien Grouped Orthogonal Arrays for Computer
Experiments with Grouped Inputs . . . . 1791--1811
Jin Xu and
Junpeng Gong and
Xiaojun Duan and
Zhengming Wang and
Xu He Sequentially Refined Latin Hypercube
Designs with Flexibly and Adaptively
Chosen Sample Sizes . . . . . . . . . . 1812--1827
Sandra R. Babyale and
Jodi Mead and
Donna Calhoun and
Patricia O. Azike Model Error Covariance Estimation for
Weak Constraint Variational Data
Assimilation . . . . . . . . . . . . . . 1828--1861
Maximilian Siebel Convergence Rates for the Maximum A
Posteriori Estimator in PDE-Regression
Models with Random Design . . . . . . . 1862--1903