OOIR: Observatory of International Research

Papers

(The TQCC of SIAM-ASA Journal on Uncertainty Quantification is 3. 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 2021-04-01 to 2025-04-01.)

Article	Citations
Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems	22
Fully Bayesian Inference for Latent Variable Gaussian Process Models	17
Multiobjective Optimization Using Expected Quantile Improvement for Decision Making in Disease Outbreaks	10
Scalable Method for Bayesian Experimental Design without Integrating over Posterior Distribution	10
Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics	9
Objective Frequentist Uncertainty Quantification for Atmospheric \(\mathrm{CO}_2\) Retrievals	9
Reduced-Order Modeling with Time-Dependent Bases for PDEs with Stochastic Boundary Conditions	9
Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions	8
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	8
Wavelet-Based Density Estimation for Persistent Homology	8
Ensemble Kalman Filters with Resampling	8
Cross-Validation--based Adaptive Sampling for Gaussian Process Models	7
Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference	7
Fast Calibration for Computer Models with Massive Physical Observations	7
Elastic Bayesian Model Calibration	6
Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions	6
Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty	6
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	6
Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification	6
The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models	6
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint	6
Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices	6
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes	6
Worst-Case Learning under a Multifidelity Model	6
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	6

Finite-Dimensional Models for Response Analysis	6
Corrigendum: “Existence and Optimality Conditions for Risk-Averse PDE-Constrained Optimization”	5
A Locally Adapted Reduced-Basis Method for Solving Risk-Averse PDE-Constrained Optimization Problems	5
Entropy-Based Burn-in Time Analysis and Ranking for (A)MCMC Algorithms in High Dimension	5
A Simple, Bias-free Approximation of Covariance Functions by the Multilevel Monte Carlo Method Having Nearly Optimal Complexity	5
Quantification of Errors Generated by Uncertain Data in a Linear Boundary Value Problem Using Neural Networks	5
Wavenumber-Explicit Parametric Holomorphy of Helmholtz Solutions in the Context of Uncertainty Quantification	5
Deep Surrogate Accelerated Delayed-Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in Rotating Disc Systems	4
One-Shot Learning of Surrogates in PDE-Constrained Optimization under Uncertainty	4
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	4
Conditional Optimal Transport on Function Spaces	4
On the Generalized Langevin Equation for Simulated Annealing	4
Space-time Multilevel Quadrature Methods and their Application for Cardiac Electrophysiology	4
Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	4
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	4
Nonparametric Posterior Learning for Emission Tomography	4
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	3
Perron–Frobenius Operator Filter for Stochastic Dynamical Systems	3
Nonlinear Reduced Models for State and Parameter Estimation	3
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	3
Corrigendum: Quasi–Monte Carlo Finite Element Analysis for Wave Propagation in Heterogeneous Random Media	3
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	3
Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy	3
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	3
Active Learning of Tree Tensor Networks using Optimal Least Squares	3
Scaled Vecchia Approximation for Fast Computer-Model Emulation	3
Adaptive Importance Sampling Based on Fault Tree Analysis for Piecewise Deterministic Markov Process	3
On the Deep Active-Subspace Method	3
Differential Equation–Constrained Optimization with Stochasticity	3
Proportional Marginal Effects for Global Sensitivity Analysis	3