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 2022-08-01 to 2026-08-01.)

Article	Citations
Ensemble Kalman Filters with Resampling	34
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	29
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions	25
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	23
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	20
Bayesian Inference for Non-synchronously Observed Diffusions	18
Tensor-Variate Gaussian Process Regression for Efficient Emulation of Complex Systems: Comparing Regressor and Covariance Structures in Outer Product and Parallel Partial Emulators	18
Conditional Optimal Transport on Function Spaces	17
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	16
Active Learning via Heteroskedastic Rational Kriging	15
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models Using Markov Chain Monte Carlo	14
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	14
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	14
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	13
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors	13
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	13
Calibration of Inexact Computer Models with Heteroscedastic Errors	12
Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning	11
Regularization for the Approximation of Functions by Mollified Discretization Methods	10
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications	10
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	10
Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems	10
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation	10
Multifidelity Surrogate Modeling for Time-Series Outputs	9
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation	9

Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation	9
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations	9
Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate	9
Multilevel Delayed Acceptance MCMC	9
Deep Learning for Model Correction of Dynamical Systems with Data Scarcity	9
Harmonizable Nonstationary Processes	9
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems	9
Bayesian Inference with Projected Densities	9
Parameter Estimation for Statistical Finite Elements	8
Calculation of Epidemic First Passage and Peak Time Probability Distributions	8
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation	8
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	8
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	8
Deterministic Kalman Filters for Dynamical Systems with Parametric Uncertainty	7
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	7
Statistical Guarantees of Group-Invariant GANs	7
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems	7
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	7
Optimally Balancing Exploration and Exploitation to Automate Multifidelity Statistical Estimation	7
Dirichlet–Neumann Averaging: The DNA of Efficient Gaussian Process Simulation	7
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations	7
On the Deep Active-Subspace Method	7
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	7
Frequency-Explicit Shape Holomorphy in Uncertainty Quantification for Acoustic Scattering	6
Proportional Marginal Effects for Global Sensitivity Analysis	6
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients	6
Discovering the Unknowns: A First Step	6
Sequentially Refined Latin Hypercube Designs with Flexibly and Adaptively Chosen Sample Sizes	6
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	6
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	6
Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators	6
Model Uncertainty and Correctability for Directed Graphical Models	6
Fast Data Inversion for High-Dimensional Ornstein–Uhlenbeck Processes from Noisy Measurements	6
Gaussian Process Regression on Nested Spaces	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