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ORCIDSIAM/ASA Journal on Uncertainty Quantification, Volume 13
Volume 13, Number 1, 2025
- Joy N. Mueller
, Khachik Sargsyan, Craig J. Daniels, Habib N. Najm:
Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty. 1-29 - Minji Kim, Kevin O'Connor, Vladas Pipiras, Themistoklis P. Sapsis:
Sampling Low-Fidelity Outputs for Estimation of High-Fidelity Density and Its Tails. 30-62 - Philip Greengard, Manas Rachh, Alex H. Barnett:
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points. 63-89 - Felix Terhag, Philipp Knechtges, Achim Basermann, Raúl Tempone:
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI. 90-113 - Vinh Hoang, Luis Espath, Sebastian Krumscheid, Raúl Tempone:
Scalable Method for Bayesian Experimental Design without Integrating over Posterior Distribution. 114-139 - Francesco A. B. Silva, Cecilia Pagliantini, Karen Veroy:
An Adaptive Hierarchical Ensemble Kalman Filter with Reduced Basis Models. 140-170 - Simon Foucart, Nicolas Hengartner:
Worst-Case Learning under a Multifidelity Model. 171-194 - Devin Francom, J. Derek Tucker, Gabriel Huerta, Kurtis Shuler, Daniel Ries:
Elastic Bayesian Model Calibration. 195-227 - Daria Semochkina, Alexander I. J. Forrester, David C. Woods:
Multiobjective Optimization Using Expected Quantile Improvement for Decision Making in Disease Outbreaks. 228-250 - Didier Chauveau, Pierre Vandekerkhove:
Entropy-Based Burn-in Time Analysis and Ranking for (A)MCMC Algorithms in High Dimension. 251-277 - Nicolaï Gouraud, Pierre Le Bris, Adrien Majka, Pierre Monmarché:
HMC and Underdamped Langevin United in the Unadjusted Convex Smooth Case. 278-303 - Bamdad Hosseini, Alexander W. Hsu, Amirhossein Taghvaei:
Conditional Optimal Transport on Function Spaces. 304-338
Volume 13, Number 2, 2025
- Jake J. Harmon, Svetlana Tokareva, Anatoly Zlotnik, Pieter J. Swart:
Adaptive Uncertainty Quantification for Stochastic Hyperbolic Conservation Laws. 339-374 - Pieter Vanmechelen, Geert Lombaert, Giovanni Samaey:
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations. 375-399 - Lea Friedli, David Ginsbourger, Arnaud Doucet, Niklas Linde:
An Energy-Based Model Approach to Rare Event Probability Estimation. 400-424 - Denis Belomestny, Tatiana Orlova:
Statistical Inference for Conservation Law McKean-Vlasov SDEs via Deep Neural Networks. 425-448 - Chih-Li Sung, Yao Song, Ying Hung:
Advancing Inverse Scattering with Surrogate Modeling and Bayesian Inference for Functional Inputs. 449-471 - Xin An, Josef Dick, Michael Feischl, Andrea Scaglioni, Thanh Tran:
Sparse Grid Approximation of Nonlinear SPDEs: The Landau-Lifshitz-Gilbert Equation. 472-517 - Xiaoli Feng, Qiang Yao, Peijun Li, Xu Wang:
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation. 518-542 - Mikhail Tsitsvero, Mingoo Jin, Andrey Lyalin:
Learning Inducing Points and Uncertainty on Molecular Data by Scalable Variational Gaussian Processes. 543-562 - Massimo Aufiero, Lucas Janson:
Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate. 563-590 - Wenzhe Xu, Daniel B. Williamson, Frederic Hourdin, Romain Roehrig:
Feature Calibration for Computer Models. 591-612 - Alex Bespalov, Dirk Praetorius, Thomas Round, Andrey Savinov:
Goal-Oriented Error Estimation and Adaptivity for Stochastic Collocation FEM. 613-638 - Tan Zhang, Zhongjian Wang, Jack Xin, Zhiwen Zhang:
A Convergent Interacting Particle Method for Computing KPP Front Speeds in Random Flows. 639-678 - Masha Naslidnyk, Motonobu Kanagawa, Toni Karvonen, 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, Dongbin Xiu:
Deep Learning for Model Correction of Dynamical Systems with Data Scarcity. 718-743 - Ahmed Attia, Sven Leyffer, Todd S. Munson:
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems. 744-774 - Fuqun Han, Stanley J. Osher, Wuchen Li:
Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators. 775-804 - Yuga Iguchi, Ajay Jasra, Mohamed Maama, Alexandros Beskos:
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications. 805-830 - Dylan Green, Jonathan Lindbloom, Anne Gelb:
Complex-Valued Signal Recovery Using a Generalized Bayesian LASSO. 831-861 - Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu:
Statistical Guarantees of Group-Invariant GANs. 862-890
Volume 13, Number 3, 2025
- Sebastian W. Ertel:
On the Mean Field Theory of Ensemble Kalman Filters for SPDEs. 891-930 - Marc Dambrine, Giulio Gargantini, Helmut Harbrecht, Jérôme Maynadier:
Shape Optimization under Constraints on the Probability of a Quadratic Functional to Exceed a Given Threshold. 931-956 - Marc Hoffmann, Camille Pouchol:
Regularization for the Approximation of Functions by Mollified Discretization Methods. 957-979 - Jingtao Zhang, Xi Chen:
Multilevel Monte Carlo Metamodeling for Variance Function Estimation. 980-1027 - René Henrion, Georg Stadler, 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, Daniel Sanz-Alonso:
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration. 1054-1084 - Jiaheng Chen, Daniel Sanz-Alonso:
Precision and Cholesky Factor Estimation for Gaussian Processes. 1085-1115 - Pu-Zhao Kow, Jenn-Nan Wang:
Consistency of Bayesian Inference for a Subdiffusion Equation. 1116-1144 - Laurence Grammont, François Bachoc, Andrés F. López-Lopera:
Error Bounds for a Kernel-Based Constrained Optimal Smoothing Approximation. 1145-1173 - Jiarui Du, Zhijian He:
Unbiased Markov Chain Quasi-Monte Carlo for Gibbs Samplers. 1174-1199 - Alex Glyn-Davies, Connor Duffin, Ieva Kazlauskaite, Mark Girolami, Ömer Deniz Akyildiz:
Statistical Finite Elements via Interacting Particle Langevin Dynamics. 1200-1227 - Philipp A. Guth, Peter Kritzer, Karl Kunisch:
Quasi-Monte Carlo Integration for Feedback Control Under Uncertainty. 1228-1264 - Lezhi Tan, Jianfeng Lu:
Accelerate Langevin Sampling with Birth-Death Process and Exploration Component. 1265-1293 - Arnulf Jentzen, 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, Qimeng Quan, Wenlong Zhang:
Stochastic Convergence Analysis of the Inverse Potential Problem. 1334-1373 - Chris Chi, Jonathan Weare, Aaron R. Dinner:
Sampling Parameters of Ordinary Differential Equations with Constrained Langevin Dynamics. 1374-1405 - Promit Chakroborty, Somayajulu L. N. Dhulipala, 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, John D. Jakeman, Tim Wildey, Troy D. Butler:
Building Population-Informed Priors for Bayesian Inference Using Data-Consistent Stochastic Inversion. 1475-1500 - Jonathan Owen, Ian Vernon:
Bayesian Emulation of Grey-Box Multimodel Ensembles Exploiting Known Interior Structure. 1501-1542 - Zhiwei Gao, 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, Kelly R. Moran, Alexander C. Murph, Jeffrey D. Hyman, Hari S. Viswanathan, Philip Stauffer:
Covariate-Informed Bifidelity Bias Correction of Distributional Output. 1616-1648 - Stephen Huan, Joseph Guinness, Matthias Katzfuss, Houman Owhadi, Florian Schäofer:
Sparse Inverse Cholesky Factorization of Dense Kernel Matrices by Greedy Conditional Selection. 1649-1679
Volume 13, Number 4, 2025
- Yuan Gao, Di Qi:
Mean Field Games for Controlling Coherent Structures in Nonlinear Fluid Systems. 1681-1708 - P. Michael Kielstra, Michael Lindsey:
A Gradient-based and Determinant-free Framework for Fully Bayesian Gaussian Process Regression. 1709-1734 - Xinzhe Zuo, Stanley J. Osher, Wuchen Li:
Gradient-Adjusted Underdamped Langevin Dynamics for Sampling. 1735-1765 - Anita Shahrokhian, Xinwei Deng, C. Devon Lin, Pritam Ranjan, Li Xu:
Adaptive Design for Contour Estimation from Computer Experiments with Quantitative and Qualitative Inputs. 1766-1790 - Wenlong Li, Jian-Feng Yang, Peter Chien:
Grouped Orthogonal Arrays for Computer Experiments with Grouped Inputs. 1791-1811 - Jin Xu, Junpeng Gong, Xiaojun Duan, Zhengming Wang, Xu He:
Sequentially Refined Latin Hypercube Designs with Flexibly and Adaptively Chosen Sample Sizes. 1812-1827 - Sandra R. Babyale, Jodi Mead, Donna Calhoun, 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 - Ralf Hiptmair, Christoph Schwab, Euan A. Spence:
Frequency-Explicit Shape Holomorphy in Uncertainty Quantification for Acoustic Scattering. 1904-1949 - Jürgen Dölz, Wei Huang, Michael D. Multerer:
\({p}\)-Multilevel Monte Carlo for Acoustic Scattering from Large Deviation Rough Random Surfaces. 1950-1971 - Tianjiao Wang, Xiang Xu, Yue Zhao:
Stability for a Stochastic Inverse Source Problem in Eddy Current Equations. 1972-1989 - Thomas E. Coons, Xun Huan:
A Multifidelity Estimator of the Expected Information Gain for Bayesian Optimal Experimental Design. 1990-2021 - Frédéric Cérou, Patrick Héas, Mathias Rousset:
Adaptive Reduced Tempering for Bayesian Inverse Problems and Rare Event Simulation. 2022-2053
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