OOIR: Observatory of International Research

Papers

(The TQCC of SIAM-ASA Journal on Uncertainty Quantification is 4. 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
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
Conditional Optimal Transport on Function Spaces	10
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	9
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
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	8
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	8
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
Calibration of Inexact Computer Models with Heteroscedastic Errors	7
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

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
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
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
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
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