OOIR: Observatory of International Research

Papers

(The TQCC of SIAM-ASA Journal on Uncertainty Quantification is 6. 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 2022-01-01 to 2026-01-01.)

Article	Citations
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	28
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	21
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	21
Cross-Validation--based Adaptive Sampling for Gaussian Process Models	17
Ensemble Kalman Filters with Resampling	16
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions	16
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	15
Conditional Optimal Transport on Function Spaces	15
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators	14
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	13
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	13
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors	12
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	12
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	12
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	12
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications	12
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	11
Calibration of Inexact Computer Models with Heteroscedastic Errors	11
Bayesian Inference of an Uncertain Generalized Diffusion Operator	11
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation	10
Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning	10
Finite Sample Approximations of Exact and Entropic Wasserstein Distances Between Covariance Operators and Gaussian Processes	9
Regularization for the Approximation of Functions by Mollified Discretization Methods	9
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems	9
Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate	9

Multilevel Delayed Acceptance MCMC	8
Deep Learning for Model Correction of Dynamical Systems with Data Scarcity	8
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation	8
Bayesian Inference with Projected Densities	8
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations	8
Harmonizable Nonstationary Processes	8
Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation	8
Calculation of Epidemic First Passage and Peak Time Probability Distributions	8
Multifidelity Surrogate Modeling for Time-Series Outputs	8
Extrapolated Polynomial Lattice Rule Integration in Computational Uncertainty Quantification	8
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	7
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation	7
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	7
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	7
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations	7
On the Deep Active-Subspace Method	6
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems	6
Discovering the Unknowns: A First Step	6
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	6
Sequentially Refined Latin Hypercube Designs with Flexibly and Adaptively Chosen Sample Sizes	6
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process	6
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	6
Statistical Guarantees of Group-Invariant GANs	6
Frequency-Explicit Shape Holomorphy in Uncertainty Quantification for Acoustic Scattering	6
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients	6
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	6
Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators	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
Proportional Marginal Effects for Global Sensitivity Analysis	6
Test Comparison for Sobol Indices over Nested Sets of Variables	6
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	6