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 2021-09-01 to 2025-09-01.)

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	18
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	17
Cross-Validation--based Adaptive Sampling for Gaussian Process Models	15
Ensemble Kalman Filters with Resampling	14
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	13
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	12
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	12
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators	12
Conditional Optimal Transport on Function Spaces	12
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	11
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	10
Calibration of Inexact Computer Models with Heteroscedastic Errors	9
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications	9
Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning	9
Bayesian Inference of an Uncertain Generalized Diffusion Operator	8
Regularization for the Approximation of Functions by Mollified Discretization Methods	8
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	8
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems	8
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation	8
Multifidelity Surrogate Modeling for Time-Series Outputs	8
Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate	7

Deep Learning for Model Correction of Dynamical Systems with Data Scarcity	7
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation	7
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations	7
Finite Sample Approximations of Exact and Entropic Wasserstein Distances Between Covariance Operators and Gaussian Processes	7
Extrapolated Polynomial Lattice Rule Integration in Computational Uncertainty Quantification	7
Harmonizable Nonstationary Processes	6
Calculation of Epidemic First Passage and Peak Time Probability Distributions	6
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	6
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations	6
Bayesian Inference with Projected Densities	6
Multilevel Delayed Acceptance MCMC	6
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	6
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	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
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	6
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation	6
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process	5
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	5
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients	5
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	5
Model Uncertainty and Correctability for Directed Graphical Models	5
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	5
Proportional Marginal Effects for Global Sensitivity Analysis	5
On the Deep Active-Subspace Method	5
Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators	5
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems	5
Statistical Guarantees of Group-Invariant GANs	5
Discovering the Unknowns: A First Step	5
Test Comparison for Sobol Indices over Nested Sets of Variables	5