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 2021-09-01 to 2025-09-01.)

Article	Citations
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions	25
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	18
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	17
Cross-Validation--based Adaptive Sampling for Gaussian Process Models	15
Ensemble Kalman Filters with Resampling	14
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	13
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	12
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators	12
Conditional Optimal Transport on Function Spaces	12
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	12
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	11
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	11
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors	10
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	10
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	10
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications	9
Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning	9
Calibration of Inexact Computer Models with Heteroscedastic Errors	9
Regularization for the Approximation of Functions by Mollified Discretization Methods	8
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	8
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems	8
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation	8
Multifidelity Surrogate Modeling for Time-Series Outputs	8
Bayesian Inference of an Uncertain Generalized Diffusion Operator	8
Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate	7

Deep Learning for Model Correction of Dynamical Systems with Data Scarcity	7
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation	7
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations	7
Finite Sample Approximations of Exact and Entropic Wasserstein Distances Between Covariance Operators and Gaussian Processes	7
Extrapolated Polynomial Lattice Rule Integration in Computational Uncertainty Quantification	7
Calculation of Epidemic First Passage and Peak Time Probability Distributions	6
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	6
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations	6
Bayesian Inference with Projected Densities	6
Multilevel Delayed Acceptance MCMC	6
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	6
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	6
Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation	6
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	6
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	6
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation	6
Harmonizable Nonstationary Processes	6
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	5
Model Uncertainty and Correctability for Directed Graphical Models	5
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	5
Proportional Marginal Effects for Global Sensitivity Analysis	5
On the Deep Active-Subspace Method	5
Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators	5
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems	5
Statistical Guarantees of Group-Invariant GANs	5
Discovering the Unknowns: A First Step	5
Test Comparison for Sobol Indices over Nested Sets of Variables	5
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process	5
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	5
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients	5
A Spline Dimensional Decomposition for Uncertainty Quantification in High Dimensions	4
Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation	4
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement	4
Gaussian Process Regression on Nested Spaces	4
An Order-Theoretic Perspective on Modes and Maximum A Posteriori Estimation in Bayesian Inverse Problems	4
Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation	4
Reliable Error Estimates for Optimal Control of Linear Elliptic PDEs with Random Inputs	4
Statistical Finite Elements via Interacting Particle Langevin Dynamics	3
Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems	3
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes	3
Low-dimensional Subspace Regularization through Structured Tensor Priors	3
Weighted Leave-One-Out Cross Validation	3
Hyperparameter Estimation for Sparse Bayesian Learning Models	3
Empirical Bayesian Inference Using a Support Informed Prior	3
Scalable Method for Bayesian Experimental Design without Integrating over Posterior Distribution	3
The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models	3
Quantifying Domain Uncertainty in Linear Elasticity	3
Shape Optimization under Constraints on the Probability of a Quadratic Functional to Exceed a Given Threshold	3
The Ensemble Kalman Filter for Rare Event Estimation	3
Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference	3

Wavelet-Based Density Estimation for Persistent Homology	3
A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit	3
A Comparative Study of Polynomial-Type Chaos Expansions for Indicator Functions	3
Projective Integral Updates for High-Dimensional Variational Inference	3
Certified Multifidelity Zeroth-Order Optimization	3
Nonparametric Posterior Learning for Emission Tomography	2
Sensitivity Analysis of Quasi-Stationary Distributions (QSDs) of Mass-Action Systems	2
Non-convergence to Global Minimizers for Adam and Stochastic Gradient Descent Optimization and Constructions of Local Minimizers in the Training of Artificial Neural Networks	2
Analysis of Nested Multilevel Monte Carlo Using Approximate Normal Random Variables	2
On Negative Transfer and Structure of Latent Functions in Multioutput Gaussian Processes	2
Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics	2
Quantification of Errors Generated by Uncertain Data in a Linear Boundary Value Problem Using Neural Networks	2
Perron–Frobenius Operator Filter for Stochastic Dynamical Systems	2
Generalized Sparse Bayesian Learning and Application to Image Reconstruction	2
A Stochastic Levenberg--Marquardt Method Using Random Models with Complexity Results	2
A Multilevel Stochastic Collocation Method for Schrödinger Equations with a Random Potential	2
Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification	2
Learning Inducing Points and Uncertainty on Molecular Data by Scalable Variational Gaussian Processes	2
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method	2
A General Framework of Rotational Sparse Approximation in Uncertainty Quantification	2
Nonasymptotic Bounds for Suboptimal Importance Sampling	2
Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty	2
Ensemble Markov Chain Monte Carlo with Teleporting Walkers	1
Parameter Selection in Gaussian Process Interpolation: An Empirical Study of Selection Criteria	1
Efficient Kriging Using Interleaved Lattice-Based Designs with Low Fill and High Separation Distance Properties	1
Accelerate Langevin Sampling with Birth-Death Process and Exploration Component	1
Superfloe Parameterization with Physics Constraints for Uncertainty Quantification of Sea Ice Floes	1
Advancing Inverse Scattering with Surrogate Modeling and Bayesian Inference for Functional Inputs	1
Stochastic Galerkin Methods for Linear Stability Analysis of Systems with Parametric Uncertainty	1
Adaptive Multilevel Subset Simulation with Selective Refinement	1
Entropy-Based Burn-in Time Analysis and Ranking for (A)MCMC Algorithms in High Dimension	1
Consistency of Bayesian Inference for a Subdiffusion Equation	1
Asymptotic Theory of \(\boldsymbol \ell _1\) -Regularized PDE Identification from a Single Noisy Trajectory	1
Covariance-Free Bifidelity Control Variates Importance Sampling for Rare Event Reliability Analysis	1
Strong Rates of Convergence of a Splitting Scheme for Schrödinger Equations with Nonlocal Interaction Cubic Nonlinearity and White Noise Dispersion	1
Noise Level Free Regularization of General Linear Inverse Problems under Unconstrained White Noise	1
Sampling Low-Fidelity Outputs for Estimation of High-Fidelity Density and Its Tails	1
Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification	1
The Bayesian Approach to Inverse Robin Problems	1
Finite-Dimensional Models for Response Analysis	1
On the Generalized Langevin Equation for Simulated Annealing	1
A Simple, Bias-free Approximation of Covariance Functions by the Multilevel Monte Carlo Method Having Nearly Optimal Complexity	1
Theoretical Guarantees for the Statistical Finite Element Method	1
Multilevel Monte Carlo Metamodeling for Variance Function Estimation	1
Precision and Cholesky Factor Estimation for Gaussian Processes	1
An Inverse Random Source Problem for the Biharmonic Wave Equation	1
Wasserstein Sensitivity of Risk and Uncertainty Propagation	1
Feature Calibration for Computer Models	1
Adaptive Uncertainty Quantification for Stochastic Hyperbolic Conservation Laws	1
Penalized Projected Kernel Calibration for Computer Models	1
Are Minimizers of the Onsager–Machlup Functional Strong Posterior Modes?	1
Space-time Multilevel Quadrature Methods and their Application for Cardiac Electrophysiology	1
Fully Bayesian Inference for Latent Variable Gaussian Process Models	1
Risk-Adapted Optimal Experimental Design	1