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 2021-08-01 to 2025-08-01.)

Article	Citations
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions	25
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	18
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	16
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	15
Ensemble Kalman Filters with Resampling	14
Cross-Validation--based Adaptive Sampling for Gaussian Process Models	13
Conditional Optimal Transport on Function Spaces	12
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators	12
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	12
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	12
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	11
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	10
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors	10
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	9
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	9
Calibration of Inexact Computer Models with Heteroscedastic Errors	8
Multifidelity Surrogate Modeling for Time-Series Outputs	8
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	8
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation	8
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems	8
Bayesian Inference of an Uncertain Generalized Diffusion Operator	8
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications	8
Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning	8
Finite Sample Approximations of Exact and Entropic Wasserstein Distances Between Covariance Operators and Gaussian Processes	7
Regularization for the Approximation of Functions by Mollified Discretization Methods	7

Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate	7
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation	7
Deep Learning for Model Correction of Dynamical Systems with Data Scarcity	6
Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation	6
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	6
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations	6
Harmonizable Nonstationary Processes	6
Bayesian Inference with Projected Densities	6
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation	6
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	6
Calculation of Epidemic First Passage and Peak Time Probability Distributions	6
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations	6
Extrapolated Polynomial Lattice Rule Integration in Computational Uncertainty Quantification	6
On the Deep Active-Subspace Method	6
Multilevel Delayed Acceptance MCMC	6
Model Uncertainty and Correctability for Directed Graphical Models	5
Proportional Marginal Effects for Global Sensitivity Analysis	5
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	5
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	5
Discovering the Unknowns: A First Step	5
Statistical Guarantees of Group-Invariant GANs	5
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	5
Test Comparison for Sobol Indices over Nested Sets of Variables	5
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	5
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	5
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems	5