OOIR: Observatory of International Research

Papers

(The median citation count of SIAM-ASA Journal on Uncertainty Quantification is 2. 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 2022-01-01 to 2026-01-01.)

Article	Citations
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	28
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	21
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	21
Cross-Validation--based Adaptive Sampling for Gaussian Process Models	17
Ensemble Kalman Filters with Resampling	16
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions	16
Conditional Optimal Transport on Function Spaces	15
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	15
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators	14
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	13
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	13
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications	12
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors	12
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	12
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	12
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	12
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	11
Calibration of Inexact Computer Models with Heteroscedastic Errors	11
Bayesian Inference of an Uncertain Generalized Diffusion Operator	11
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation	10
Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning	10
Finite Sample Approximations of Exact and Entropic Wasserstein Distances Between Covariance Operators and Gaussian Processes	9
Regularization for the Approximation of Functions by Mollified Discretization Methods	9
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems	9
Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate	9

Multilevel Delayed Acceptance MCMC	8
Deep Learning for Model Correction of Dynamical Systems with Data Scarcity	8
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation	8
Bayesian Inference with Projected Densities	8
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations	8
Harmonizable Nonstationary Processes	8
Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation	8
Calculation of Epidemic First Passage and Peak Time Probability Distributions	8
Multifidelity Surrogate Modeling for Time-Series Outputs	8
Extrapolated Polynomial Lattice Rule Integration in Computational Uncertainty Quantification	8
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	7
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations	7
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	7
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation	7
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	7
Test Comparison for Sobol Indices over Nested Sets of Variables	6
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	6
On the Deep Active-Subspace Method	6
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems	6
Discovering the Unknowns: A First Step	6
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	6
Sequentially Refined Latin Hypercube Designs with Flexibly and Adaptively Chosen Sample Sizes	6
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process	6
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	6
Statistical Guarantees of Group-Invariant GANs	6
Frequency-Explicit Shape Holomorphy in Uncertainty Quantification for Acoustic Scattering	6
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients	6
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	6
Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators	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
Proportional Marginal Effects for Global Sensitivity Analysis	6
Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation	5
An Order-Theoretic Perspective on Modes and Maximum A Posteriori Estimation in Bayesian Inverse Problems	5
Projective Integral Updates for High-Dimensional Variational Inference	5
Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation	5
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement	5
Low-dimensional Subspace Regularization through Structured Tensor Priors	5
Certified Multifidelity Zeroth-Order Optimization	5
Gaussian Process Regression on Nested Spaces	5
A Spline Dimensional Decomposition for Uncertainty Quantification in High Dimensions	5
Quantifying Domain Uncertainty in Linear Elasticity	5
Reliable Error Estimates for Optimal Control of Linear Elliptic PDEs with Random Inputs	4
Shape Optimization under Constraints on the Probability of a Quadratic Functional to Exceed a Given Threshold	4
Empirical Bayesian Inference Using a Support Informed Prior	4
A Comparative Study of Polynomial-Type Chaos Expansions for Indicator Functions	4
The Ensemble Kalman Filter for Rare Event Estimation	4
Weighted Leave-One-Out Cross Validation	4
Wavelet-Based Density Estimation for Persistent Homology	4
Scalable Method for Bayesian Experimental Design without Integrating over Posterior Distribution	4

Hyperparameter Estimation for Sparse Bayesian Learning Models	4
Statistical Finite Elements via Interacting Particle Langevin Dynamics	4
A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit	4
Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference	4
Nonparametric Posterior Learning for Emission Tomography	3
Perron–Frobenius Operator Filter for Stochastic Dynamical Systems	3
A General Framework of Rotational Sparse Approximation in Uncertainty Quantification	3
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes	3
A Stochastic Levenberg--Marquardt Method Using Random Models with Complexity Results	3
Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty	3
Sensitivity Analysis of Quasi-Stationary Distributions (QSDs) of Mass-Action Systems	3
Quantification of Errors Generated by Uncertain Data in a Linear Boundary Value Problem Using Neural Networks	3
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method	3
Non-convergence to Global Minimizers for Adam and Stochastic Gradient Descent Optimization and Constructions of Local Minimizers in the Training of Artificial Neural Networks	3
Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems	3
Learning Inducing Points and Uncertainty on Molecular Data by Scalable Variational Gaussian Processes	3
Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification	3
The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models	3
Generalized Sparse Bayesian Learning and Application to Image Reconstruction	3
Covariate-Informed Bifidelity Bias Correction of Distributional Output	3
Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics	3
Precision and Cholesky Factor Estimation for Gaussian Processes	2
Nonasymptotic Bounds for Suboptimal Importance Sampling	2
Noise Level Free Regularization of General Linear Inverse Problems under Unconstrained White Noise	2
Superfloe Parameterization with Physics Constraints for Uncertainty Quantification of Sea Ice Floes	2
Risk-Adapted Optimal Experimental Design	2
Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification	2
Asymptotic Theory of \(\boldsymbol \ell _1\) -Regularized PDE Identification from a Single Noisy Trajectory	2
Multilevel Monte Carlo Metamodeling for Variance Function Estimation	2
Theoretical Guarantees for the Statistical Finite Element Method	2
Sampling Low-Fidelity Outputs for Estimation of High-Fidelity Density and Its Tails	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
A Multilevel Stochastic Collocation Method for Schrödinger Equations with a Random Potential	2
An Inverse Random Source Problem for the Biharmonic Wave Equation	2
Covariance-Free Bifidelity Control Variates Importance Sampling for Rare Event Reliability Analysis	2
Finite-Dimensional Models for Response Analysis	2
A Multifidelity Estimator of the Expected Information Gain for Bayesian Optimal Experimental Design	2
Mean Field Games for Controlling Coherent Structures in Nonlinear Fluid Systems	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
Wasserstein Sensitivity of Risk and Uncertainty Propagation	2
Analysis of Nested Multilevel Monte Carlo Using Approximate Normal Random Variables	2
Parameter Selection in Gaussian Process Interpolation: An Empirical Study of Selection Criteria	2
Fully Bayesian Inference for Latent Variable Gaussian Process Models	2
Feature Calibration for Computer Models	2
Sparse Inverse Cholesky Factorization of Dense Kernel Matrices by Greedy Conditional Selection	2
Accelerate Langevin Sampling with Birth-Death Process and Exploration Component	2