OOIR: Observatory of International Research

Papers

(The median citation count of SIAM-ASA Journal on Uncertainty Quantification is 1. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-04-01 to 2025-04-01.)

Article	Citations
Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems	22
Fully Bayesian Inference for Latent Variable Gaussian Process Models	17
Scalable Method for Bayesian Experimental Design without Integrating over Posterior Distribution	10
Multiobjective Optimization Using Expected Quantile Improvement for Decision Making in Disease Outbreaks	10
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions	9
Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics	9
Objective Frequentist Uncertainty Quantification for Atmospheric \(\mathrm{CO}_2\) Retrievals	9
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	8
Wavelet-Based Density Estimation for Persistent Homology	8
Ensemble Kalman Filters with Resampling	8
Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions	8
Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference	7
Fast Calibration for Computer Models with Massive Physical Observations	7
Cross-Validation--based Adaptive Sampling for Gaussian Process Models	7
The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models	6
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint	6
Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices	6
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes	6
Worst-Case Learning under a Multifidelity Model	6
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	6
Finite-Dimensional Models for Response Analysis	6
Elastic Bayesian Model Calibration	6
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	6
Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty	6
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	6

Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification	6
Quantification of Errors Generated by Uncertain Data in a Linear Boundary Value Problem Using Neural Networks	5
Wavenumber-Explicit Parametric Holomorphy of Helmholtz Solutions in the Context of Uncertainty Quantification	5
Corrigendum: “Existence and Optimality Conditions for Risk-Averse PDE-Constrained Optimization”	5
A Locally Adapted Reduced-Basis Method for Solving Risk-Averse PDE-Constrained Optimization Problems	5
Entropy-Based Burn-in Time Analysis and Ranking for (A)MCMC Algorithms in High Dimension	5
A Simple, Bias-free Approximation of Covariance Functions by the Multilevel Monte Carlo Method Having Nearly Optimal Complexity	5
Space-time Multilevel Quadrature Methods and their Application for Cardiac Electrophysiology	4
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	4
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	4
Nonparametric Posterior Learning for Emission Tomography	4
Deep Surrogate Accelerated Delayed-Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in Rotating Disc Systems	4
One-Shot Learning of Surrogates in PDE-Constrained Optimization under Uncertainty	4
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	4
Conditional Optimal Transport on Function Spaces	4
On the Generalized Langevin Equation for Simulated Annealing	4
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	3
Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy	3
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	3
Active Learning of Tree Tensor Networks using Optimal Least Squares	3
Scaled Vecchia Approximation for Fast Computer-Model Emulation	3
Adaptive Importance Sampling Based on Fault Tree Analysis for Piecewise Deterministic Markov Process	3
On the Deep Active-Subspace Method	3
Differential Equation–Constrained Optimization with Stochasticity	3
Proportional Marginal Effects for Global Sensitivity Analysis	3
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	3
Perron–Frobenius Operator Filter for Stochastic Dynamical Systems	3
Nonlinear Reduced Models for State and Parameter Estimation	3
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	3
Corrigendum: Quasi–Monte Carlo Finite Element Analysis for Wave Propagation in Heterogeneous Random Media	3
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators	2
Are Minimizers of the Onsager–Machlup Functional Strong Posterior Modes?	2
Penalized Projected Kernel Calibration for Computer Models	2
Monte Carlo Methods for the Neutron Transport Equation	2
Test Comparison for Sobol Indices over Nested Sets of Variables	2
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	2
A General Framework of Rotational Sparse Approximation in Uncertainty Quantification	2
Stochastic Galerkin Methods for Linear Stability Analysis of Systems with Parametric Uncertainty	2
A Combination Technique for Optimal Control Problems Constrained by Random PDEs	2
Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification	2
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors	2
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	2
Sensitivity Analysis of Quasi-Stationary Distributions (QSDs) of Mass-Action Systems	2
Model Uncertainty and Correctability for Directed Graphical Models	2
Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification	2
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	2
Landmark-Warped Emulators for Models with Misaligned Functional Response	2
Analysis of Nested Multilevel Monte Carlo Using Approximate Normal Random Variables	1
Calibration of Inexact Computer Models with Heteroscedastic Errors	1
An Inverse Random Source Problem for the Biharmonic Wave Equation	1

Deep Learning in High Dimension: Neural Network Expression Rates for Analytic Functions in \(\pmb{L^2(\mathbb{R}^d,\gamma_d)}\)	1
Conglomerate Multi-fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions	1
Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference	1
Discovering the Unknowns: A First Step	1
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	1
Generalized Sparse Bayesian Learning and Application to Image Reconstruction	1
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients	1
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement	1
Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation	1
Nonasymptotic Bounds for Suboptimal Importance Sampling	1
Data-Driven Rules for Multidimensional Reflection Problems	1
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method	1
Adaptive Multilevel Subset Simulation with Selective Refinement	1
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	1
Gaussian Process Regression on Nested Spaces	1
Spectral Convergence of a Semi-discretized Numerical System for the Spatially Homogeneous Boltzmann Equation with Uncertainties	1
Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems	1
An Order-Theoretic Perspective on Modes and Maximum A Posteriori Estimation in Bayesian Inverse Problems	1
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	1
Multiple Closed Curve Modeling with Uncertainty Quantification for Shape Analysis	1
The Bayesian Approach to Inverse Robin Problems	1
A Stochastic Levenberg--Marquardt Method Using Random Models with Complexity Results	1
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process	1