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 2021-11-01 to 2025-11-01.)

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	20
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	19
Ensemble Kalman Filters with Resampling	17
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	16
Cross-Validation--based Adaptive Sampling for Gaussian Process Models	15
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	14
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators	13
Conditional Optimal Transport on Function Spaces	13
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	12
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	12
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	12
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	11
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors	11
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	11
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	10
Calibration of Inexact Computer Models with Heteroscedastic Errors	10
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications	10
Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning	10
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations	9
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems	9
Harmonizable Nonstationary Processes	9
Bayesian Inference of an Uncertain Generalized Diffusion Operator	9
Deep Learning for Model Correction of Dynamical Systems with Data Scarcity	9
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation	9

Multifidelity Surrogate Modeling for Time-Series Outputs	8
Regularization for the Approximation of Functions by Mollified Discretization Methods	8
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation	8
Extrapolated Polynomial Lattice Rule Integration in Computational Uncertainty Quantification	8
Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate	8
Finite Sample Approximations of Exact and Entropic Wasserstein Distances Between Covariance Operators and Gaussian Processes	8
Calculation of Epidemic First Passage and Peak Time Probability Distributions	7
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	7
Bayesian Inference with Projected Densities	7
Multilevel Delayed Acceptance MCMC	7
Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation	7
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	6
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations	6
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	6
Model Uncertainty and Correctability for Directed Graphical Models	6
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems	6
On the Deep Active-Subspace Method	6
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	6
Discovering the Unknowns: A First Step	6
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation	6
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	6
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	6
Statistical Guarantees of Group-Invariant GANs	6
Test Comparison for Sobol Indices over Nested Sets of Variables	6
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process	5
Proportional Marginal Effects for Global Sensitivity Analysis	5
Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation	5
Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators	5
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients	5
Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation	5
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	5
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	5
Sequentially Refined Latin Hypercube Designs with Flexibly and Adaptively Chosen Sample Sizes	5
Gaussian Process Regression on Nested Spaces	5
Hyperparameter Estimation for Sparse Bayesian Learning Models	4
Weighted Leave-One-Out Cross Validation	4
Low-dimensional Subspace Regularization through Structured Tensor Priors	4
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement	4
Reliable Error Estimates for Optimal Control of Linear Elliptic PDEs with Random Inputs	4
Statistical Finite Elements via Interacting Particle Langevin Dynamics	4
An Order-Theoretic Perspective on Modes and Maximum A Posteriori Estimation in Bayesian Inverse Problems	4
Certified Multifidelity Zeroth-Order Optimization	4
Quantifying Domain Uncertainty in Linear Elasticity	4
Shape Optimization under Constraints on the Probability of a Quadratic Functional to Exceed a Given Threshold	4
A Spline Dimensional Decomposition for Uncertainty Quantification in High Dimensions	4
Projective Integral Updates for High-Dimensional Variational Inference	4
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
Covariate-Informed Bifidelity Bias Correction of Distributional Output	3

Quantification of Errors Generated by Uncertain Data in a Linear Boundary Value Problem Using Neural Networks	3
A Comparative Study of Polynomial-Type Chaos Expansions for Indicator Functions	3
A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit	3
Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference	3
Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics	3
Learning Inducing Points and Uncertainty on Molecular Data by Scalable Variational Gaussian Processes	3
Perron–Frobenius Operator Filter for Stochastic Dynamical Systems	3
Wavelet-Based Density Estimation for Persistent Homology	3
The Ensemble Kalman Filter for Rare Event Estimation	3
Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems	3
Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty	3
Nonparametric Posterior Learning for Emission Tomography	3
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes	3
Nonasymptotic Bounds for Suboptimal Importance Sampling	2
Sensitivity Analysis of Quasi-Stationary Distributions (QSDs) of Mass-Action Systems	2
Generalized Sparse Bayesian Learning and Application to Image Reconstruction	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
Precision and Cholesky Factor Estimation for Gaussian Processes	2
Strong Rates of Convergence of a Splitting Scheme for Schrödinger Equations with Nonlocal Interaction Cubic Nonlinearity and White Noise Dispersion	2
On Negative Transfer and Structure of Latent Functions in Multioutput Gaussian Processes	2
A Stochastic Levenberg--Marquardt Method Using Random Models with Complexity Results	2
A General Framework of Rotational Sparse Approximation in Uncertainty Quantification	2
Analysis of Nested Multilevel Monte Carlo Using Approximate Normal Random Variables	2
Noise Level Free Regularization of General Linear Inverse Problems under Unconstrained White Noise	2
Sampling Low-Fidelity Outputs for Estimation of High-Fidelity Density and Its Tails	2
An Inverse Random Source Problem for the Biharmonic Wave Equation	2
Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification	2
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method	2
A Multilevel Stochastic Collocation Method for Schrödinger Equations with a Random Potential	2
Wasserstein Sensitivity of Risk and Uncertainty Propagation	2
Ensemble Markov Chain Monte Carlo with Teleporting Walkers	2
Efficient Kriging Using Interleaved Lattice-Based Designs with Low Fill and High Separation Distance Properties	2
Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification	1
Risk-Adapted Optimal Experimental Design	1
Multilevel Monte Carlo Metamodeling for Variance Function Estimation	1
Parameter Selection in Gaussian Process Interpolation: An Empirical Study of Selection Criteria	1
Asymptotic Theory of \(\boldsymbol \ell _1\) -Regularized PDE Identification from a Single Noisy Trajectory	1
Adaptive Multilevel Subset Simulation with Selective Refinement	1
An Approximate Control Variates Approach to Multifidelity Distribution Estimation	1
Hierarchical Shrinkage Gaussian Processes: Applications to Computer Code Emulation and Dynamical System Recovery	1
Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations	1
Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference	1
The Bayesian Approach to Inverse Robin Problems	1
Consistency of Bayesian Inference for a Subdiffusion Equation	1
Fully Bayesian Inference for Latent Variable Gaussian Process Models	1
Covariance-Free Bifidelity Control Variates Importance Sampling for Rare Event Reliability Analysis	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
On the Generalized Langevin Equation for Simulated Annealing	1
A Hybrid Gibbs Sampler for Edge-Preserving Tomographic Reconstruction with Uncertain View Angles	1
Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks	1
Penalized Projected Kernel Calibration for Computer Models	1
Are Minimizers of the Onsager–Machlup Functional Strong Posterior Modes?	1
Stochastic Galerkin Methods for Linear Stability Analysis of Systems with Parametric Uncertainty	1
A Simple, Bias-free Approximation of Covariance Functions by the Multilevel Monte Carlo Method Having Nearly Optimal Complexity	1
Adaptive Design for Contour Estimation from Computer Experiments with Quantitative and Qualitative Inputs	1
Entropy-Based Burn-in Time Analysis and Ranking for (A)MCMC Algorithms in High Dimension	1
Finite-Dimensional Models for Response Analysis	1
Sparse Inverse Cholesky Factorization of Dense Kernel Matrices by Greedy Conditional Selection	1
Theoretical Guarantees for the Statistical Finite Element Method	1
Space-time Multilevel Quadrature Methods and their Application for Cardiac Electrophysiology	1
Mean Field Games for Controlling Coherent Structures in Nonlinear Fluid Systems	1
Gradient-Adjusted Underdamped Langevin Dynamics for Sampling	1
HMC and Underdamped Langevin United in the Unadjusted Convex Smooth Case	1
Adaptive Uncertainty Quantification for Stochastic Hyperbolic Conservation Laws	1
Data-Driven Rules for Multidimensional Reflection Problems	1
Advancing Inverse Scattering with Surrogate Modeling and Bayesian Inference for Functional Inputs	1
Feature Calibration for Computer Models	1