OOIR: Observatory of International Research

Papers

(The TQCC of SIAM-ASA Journal on Uncertainty Quantification is 5. 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
Conditional Optimal Transport on Function Spaces	13
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators	13
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	12
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	12
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	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
Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation	7
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
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
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
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