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 2022-05-01 to 2026-05-01.)

Article	Citations
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	30
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	28
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions	22
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	20
Ensemble Kalman Filters with Resampling	19
Bayesian Inference for Non-synchronously Observed Diffusions	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	13
Tensor-Variate Gaussian Process Regression for Efficient Emulation of Complex Systems: Comparing Regressor and Covariance Structures in Outer Product and Parallel Partial Emulators	13
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	13
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	12
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	11
Calibration of Inexact Computer Models with Heteroscedastic Errors	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
Leveraging Viscous Hamilton–Jacobi PDEs for Uncertainty Quantification in Scientific Machine Learning	10
Asymptotic Bounds for Smoothness Parameter Estimates in Gaussian Process Interpolation	10
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	10
Robust Kalman and Bayesian Set-Valued Filtering and Model Validation for Linear Stochastic Systems	9
Regularization for the Approximation of Functions by Mollified Discretization Methods	9
Deep Learning for Model Correction of Dynamical Systems with Data Scarcity	9
Gradient-Free Sequential Bayesian Experimental Design via Interacting Particle Systems	9
Harmonizable Nonstationary Processes	9

Surrogate-Based Global Sensitivity Analysis with Statistical Guarantees via Floodgate	9
Antithetic Multilevel Methods for Elliptic and Hypoelliptic Diffusions with Applications	9
Extrapolated Polynomial Lattice Rule Integration in Computational Uncertainty Quantification	9
Parameter Inference Based on Gaussian Processes Informed by Nonlinear Partial Differential Equations	9
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	8
Bayesian Inference with Projected Densities	8
Multifidelity Surrogate Modeling for Time-Series Outputs	8
Calculation of Epidemic First Passage and Peak Time Probability Distributions	8
Uniform Error Bounds of the Ensemble Transform Kalman Filter for Chaotic Dynamics with Multiplicative Covariance Inflation	8
Multilevel Delayed Acceptance MCMC	8
Generalized Bayesian MARS: Tools for Stochastic Computer Model Emulation	8
Dirichlet–Neumann Averaging: The DNA of Efficient Gaussian Process Simulation	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
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	7
Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems	7
On the Deep Active-Subspace Method	7
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	7
An Inverse Source Problem for the Stochastic Multiterm Time-Fractional Diffusion-Wave Equation	7
Multilevel Markov Chain Monte Carlo with Likelihood Scaling for Bayesian Inversion with High-resolution Observations	7
Model Uncertainty and Correctability for Directed Graphical Models	6
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	6
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
Test Comparison for Sobol Indices over Nested Sets of Variables	6
Statistical Guarantees of Group-Invariant GANs	6
Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators	6
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	6
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process	6