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 2021-05-01 to 2025-05-01.)

Article	Citations
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	23
Cross-Validation--based Adaptive Sampling for Gaussian Process Models	17
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	15
Ensemble Kalman Filters with Resampling	14
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	11
Conditional Optimal Transport on Function Spaces	10
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	10
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors	10
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions	10
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	9
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	8
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	8
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	8
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators	8
Calibration of Inexact Computer Models with Heteroscedastic Errors	7
Bayesian Inference of an Uncertain Generalized Diffusion Operator	7
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation	7
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	7
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	7
Bayesian Inference with Projected Densities	6
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation	6
Finite Sample Approximations of Exact and Entropic Wasserstein Distances Between Covariance Operators and Gaussian Processes	6
Extrapolated Polynomial Lattice Rule Integration in Computational Uncertainty Quantification	6
Multifidelity Surrogate Modeling for Time-Series Outputs	6
Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation	6

Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning	6
Harmonizable Nonstationary Processes	6
Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate	6
Multilevel Delayed Acceptance MCMC	6
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems	6
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations	6
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation	5
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	5
Calculation of Epidemic First Passage and Peak Time Probability Distributions	5
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	5
On the Deep Active-Subspace Method	5
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	5
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	5
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations	5
Discovering the Unknowns: A First Step	4
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	4
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	4
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients	4
Test Comparison for Sobol Indices over Nested Sets of Variables	4
Proportional Marginal Effects for Global Sensitivity Analysis	4
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	4
Model Uncertainty and Correctability for Directed Graphical Models	4
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process	4
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	4
Empirical Bayesian Inference Using a Support Informed Prior	3
A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit	3
Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation	3
Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation	3
Reliable Error Estimates for Optimal Control of Linear Elliptic PDEs with Random Inputs	3
Hyperparameter Estimation for Sparse Bayesian Learning Models	3
A Comparative Study of Polynomial-Type Chaos Expansions for Indicator Functions	3
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes	3
Gaussian Process Regression on Nested Spaces	3
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement	3
Quantifying Domain Uncertainty in Linear Elasticity	3
Weighted Leave-One-Out Cross Validation	3
The Ensemble Kalman Filter for Rare Event Estimation	3
Certified Multifidelity Zeroth-Order Optimization	3
An Order-Theoretic Perspective on Modes and Maximum A Posteriori Estimation in Bayesian Inverse Problems	3
A Spline Dimensional Decomposition for Uncertainty Quantification in High Dimensions	3
Projective Integral Updates for High-Dimensional Variational Inference	3
Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference	2
The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models	2
Nonparametric Posterior Learning for Emission Tomography	2
Learning Inducing Points and Uncertainty on Molecular Data by Scalable Variational Gaussian Processes	2
Sensitivity Analysis of Quasi-Stationary Distributions (QSDs) of Mass-Action Systems	2
Scalable Method for Bayesian Experimental Design without Integrating over Posterior Distribution	2
Wavelet-Based Density Estimation for Persistent Homology	2
Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics	2
A General Framework of Rotational Sparse Approximation in Uncertainty Quantification	2

Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification	2
Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty	2
Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems	2
Quantification of Errors Generated by Uncertain Data in a Linear Boundary Value Problem Using Neural Networks	2
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method	2
Perron–Frobenius Operator Filter for Stochastic Dynamical Systems	2
A Simple, Bias-free Approximation of Covariance Functions by the Multilevel Monte Carlo Method Having Nearly Optimal Complexity	1
Asymptotic Theory of \(\boldsymbol \ell _1\) -Regularized PDE Identification from a Single Noisy Trajectory	1
Efficient Kriging Using Interleaved Lattice-Based Designs with Low Fill and High Separation Distance Properties	1
Wasserstein Sensitivity of Risk and Uncertainty Propagation	1
A Stochastic Levenberg--Marquardt Method Using Random Models with Complexity Results	1
On Negative Transfer and Structure of Latent Functions in Multioutput Gaussian Processes	1
Parameter Selection in Gaussian Process Interpolation: An Empirical Study of Selection Criteria	1
Feature Calibration for Computer Models	1
Risk-Adapted Optimal Experimental Design	1
Sampling Low-Fidelity Outputs for Estimation of High-Fidelity Density and Its Tails	1
Nonasymptotic Bounds for Suboptimal Importance Sampling	1
Generalized Sparse Bayesian Learning and Application to Image Reconstruction	1
Analysis of Nested Multilevel Monte Carlo Using Approximate Normal Random Variables	1
Superfloe Parameterization with Physics Constraints for Uncertainty Quantification of Sea Ice Floes	1
Space-time Multilevel Quadrature Methods and their Application for Cardiac Electrophysiology	1
Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification	1
Finite-Dimensional Models for Response Analysis	1
Theoretical Guarantees for the Statistical Finite Element Method	1
Fully Bayesian Inference for Latent Variable Gaussian Process Models	1
A Multilevel Stochastic Collocation Method for Schrödinger Equations with a Random Potential	1
An Inverse Random Source Problem for the Biharmonic Wave Equation	1
Noise Level Free Regularization of General Linear Inverse Problems under Unconstrained White Noise	1
Strong Rates of Convergence of a Splitting Scheme for Schrödinger Equations with Nonlocal Interaction Cubic Nonlinearity and White Noise Dispersion	1
Ensemble Markov Chain Monte Carlo with Teleporting Walkers	1
Entropy-Based Burn-in Time Analysis and Ranking for (A)MCMC Algorithms in High Dimension	1