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 2022-06-01 to 2026-06-01.)

Article	Citations
Ensemble Kalman Filters with Resampling	31
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	28
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	24
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	21
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions	21
Tensor-Variate Gaussian Process Regression for Efficient Emulation of Complex Systems: Comparing Regressor and Covariance Structures in Outer Product and Parallel Partial Emulators	18
Bayesian Inference for Non-synchronously Observed Diffusions	17
Conditional Optimal Transport on Function Spaces	17
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models Using Markov Chain Monte Carlo	15
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	13
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	13
Active Learning via Heteroskedastic Rational Kriging	13
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	12
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	12
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors	12
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	11
Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning	11
Calibration of Inexact Computer Models with Heteroscedastic Errors	11
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	10
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation	10
Regularization for the Approximation of Functions by Mollified Discretization Methods	9
Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems	9
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations	9
Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate	9
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications	9

Deep Learning for Model Correction of Dynamical Systems with Data Scarcity	9
Harmonizable Nonstationary Processes	9
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems	9
Extrapolated Polynomial Lattice Rule Integration in Computational Uncertainty Quantification	9
Multifidelity Surrogate Modeling for Time-Series Outputs	9
Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation	8
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	8
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations	8
Multilevel Delayed Acceptance MCMC	8
Bayesian Inference with Projected Densities	8
Dirichlet–Neumann Averaging: The DNA of Efficient Gaussian Process Simulation	8
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	8
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation	8
Calculation of Epidemic First Passage and Peak Time Probability Distributions	8
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	8
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation	8
On the Deep Active-Subspace Method	7
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems	7
Deterministic Kalman Filters for Dynamical Systems with Parametric Uncertainty	7
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	7
Statistical Guarantees of Group-Invariant GANs	7
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	7
Model Uncertainty and Correctability for Directed Graphical Models	7
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	6
Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators	6
Sequentially Refined Latin Hypercube Designs with Flexibly and Adaptively Chosen Sample Sizes	6
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	6
Test Comparison for Sobol Indices over Nested Sets of Variables	6
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients	6
Discovering the Unknowns: A First Step	6
Frequency-Explicit Shape Holomorphy in Uncertainty Quantification for Acoustic Scattering	6
Proportional Marginal Effects for Global Sensitivity Analysis	6
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process	6
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	6
An Order-Theoretic Perspective on Modes and Maximum A Posteriori Estimation in Bayesian Inverse Problems	5
Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation	5
Analysis of a Computational Framework for Bayesian Inverse Problems: Ensemble Kalman Updates and MAP Estimators under Mesh Refinement	5
Gaussian Process Regression on Nested Spaces	5
Domain Uncertainty Quantification for the Lippmann–Schwinger Volume Integral Equation	5
Subspace Splitting Fast Sampling from Gaussian Posterior Distributions of Linear Inverse Problems	5
Quantifying Domain Uncertainty in Linear Elasticity	4
Dimension Free Nonasymptotic Bounds on the Accuracy of High-Dimensional Laplace Approximation	4
Low-dimensional Subspace Regularization through Structured Tensor Priors	4
A Comparative Study of Polynomial-Type Chaos Expansions for Indicator Functions	4
A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit	4
Projective Integral Updates for High-Dimensional Variational Inference	4
Certified Multifidelity Zeroth-Order Optimization	4
Weighted Leave-One-Out Cross Validation	4
Empirical Bayesian Inference Using a Support Informed Prior	4
Scalable Method for Bayesian Experimental Design without Integrating over Posterior Distribution	4

High-Dimensional Stochastic Finite Volumes Using the Tensor Train Format	4
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
Statistical Finite Elements via Interacting Particle Langevin Dynamics	4
Hyperparameter Estimation for Sparse Bayesian Learning Models	4
Sensitivity Analysis of Quasi-Stationary Distributions (QSDs) of Mass-Action Systems	3
Local Sensitivity Analysis for Bayesian Inverse Problems	3
Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics	3
Nonparametric Posterior Learning for Emission Tomography	3
The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models	3
Wavelet-Based Density Estimation for Persistent Homology	3
Generalized Sparse Bayesian Learning and Application to Image Reconstruction	3
Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty	3
Efficient Kriging Using Interleaved Lattice-Based Designs with Low Fill and High Separation Distance Properties	3
A General Framework of Rotational Sparse Approximation in Uncertainty Quantification	3
Nonasymptotic Bounds for Suboptimal Importance Sampling	3
Random Fourier Features Based Gaussian Process Models for Stochastic Simulations	3
Learning Inducing Points and Uncertainty on Molecular Data by Scalable Variational Gaussian Processes	3
Quantification of Errors Generated by Uncertain Data in a Linear Boundary Value Problem Using Neural Networks	3
Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems	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
Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification	3
Wasserstein Sensitivity of Risk and Uncertainty Propagation	3
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method	3
An Inverse Random Source Problem for the Biharmonic Wave Equation	3
Covariate-Informed Bifidelity Bias Correction of Distributional Output	3
Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference	3
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes	3
Perron–Frobenius Operator Filter for Stochastic Dynamical Systems	3
A Multilevel Stochastic Collocation Method for Schrödinger Equations with a Random Potential	3
On Negative Transfer and Structure of Latent Functions in Multioutput Gaussian Processes	2
Noise Level Free Regularization of General Linear Inverse Problems under Unconstrained White Noise	2
Ensemble Markov Chain Monte Carlo with Teleporting Walkers	2
Accelerate Langevin Sampling with Birth-Death Process and Exploration Component	2
Asymptotic Theory of \(\boldsymbol \ell _1\) -Regularized PDE Identification from a Single Noisy Trajectory	2
Neural Field Equations with Random Data	2
Precision and Cholesky Factor Estimation for Gaussian Processes	2
Mean Field Games for Controlling Coherent Structures in Nonlinear Fluid Systems	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
Neural Network Approaches for Variance Reduction in Fluctuation Formulas	2
Superfloe Parameterization with Physics Constraints for Uncertainty Quantification of Sea Ice Floes	2
Parameter Selection in Gaussian Process Interpolation: An Empirical Study of Selection Criteria	2
Sparse Inverse Cholesky Factorization of Dense Kernel Matrices by Greedy Conditional Selection	2
Covariance-Free Bifidelity Control Variates Importance Sampling for Rare Event Reliability Analysis	2