OOIR: Observatory of International Research

Papers

(The median citation count of SIAM-ASA Journal on Uncertainty Quantification is 1. 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 2021-02-01 to 2025-02-01.)

Article	Citations
Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems	21
Fully Bayesian Inference for Latent Variable Gaussian Process Models	17
Scalable Method for Bayesian Experimental Design without Integrating over Posterior Distribution	10
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
Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics	9
Ensemble Kalman Filters with Resampling	8
Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions	8
Projected Wasserstein Gradient Descent for High-Dimensional Bayesian Inference	8
Wavelet-Based Density Estimation for Persistent Homology	8
Fast Calibration for Computer Models with Massive Physical Observations	7
Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification	7
Cross-Validation--based Adaptive Sampling for Gaussian Process Models	7
Complete Deterministic Dynamics and Spectral Decomposition of the Linear Ensemble Kalman Inversion	6
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint	6
The Zero Problem: Gaussian Process Emulators for Range-Constrained Computer Models	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
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes	6
Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices	6
Adaptive Operator Learning for Infinite-Dimensional Bayesian Inverse Problems	5
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	5
Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy	5
Finite-Dimensional Models for Response Analysis	5
A Simple, Bias-free Approximation of Covariance Functions by the Multilevel Monte Carlo Method Having Nearly Optimal Complexity	5

Robust Level-Set-Based Topology Optimization Under Uncertainties Using Anchored ANOVA Petrov–Galerkin Method	4
Space-time Multilevel Quadrature Methods and their Application for Cardiac Electrophysiology	4
Quantifying the Effect of Random Dispersion for Logarithmic Schrödinger Equation	4
A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration	4
A Locally Adapted Reduced-Basis Method for Solving Risk-Averse PDE-Constrained Optimization Problems	4
Corrigendum: “Existence and Optimality Conditions for Risk-Averse PDE-Constrained Optimization”	4
One-Shot Learning of Surrogates in PDE-Constrained Optimization under Uncertainty	4
Scaled Vecchia Approximation for Fast Computer-Model Emulation	4
Quantification of Errors Generated by Uncertain Data in a Linear Boundary Value Problem Using Neural Networks	4
On the Generalized Langevin Equation for Simulated Annealing	4
Wavenumber-Explicit Parametric Holomorphy of Helmholtz Solutions in the Context of Uncertainty Quantification	4
Perron–Frobenius Operator Filter for Stochastic Dynamical Systems	3
Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings	3
A General Framework of Rotational Sparse Approximation in Uncertainty Quantification	3
Stochastic Galerkin Methods for Linear Stability Analysis of Systems with Parametric Uncertainty	3
Nonparametric Posterior Learning for Emission Tomography	3
On the Deep Active-Subspace Method	3
Adaptive Importance Sampling Based on Fault Tree Analysis for Piecewise Deterministic Markov Process	3
Model Uncertainty and Correctability for Directed Graphical Models	3
Deep Surrogate Accelerated Delayed-Acceptance Hamiltonian Monte Carlo for Bayesian Inference of Spatio-Temporal Heat Fluxes in Rotating Disc Systems	3
Equispaced Fourier Representations for Efficient Gaussian Process Regression from a Billion Data Points	3
Nonlinear Reduced Models for State and Parameter Estimation	3
Generalized Sparse Bayesian Learning and Application to Image Reconstruction	3
Test Comparison for Sobol Indices over Nested Sets of Variables	3
Penalized Projected Kernel Calibration for Computer Models	2
Nonparametric Estimation for Independent and Identically Distributed Stochastic Differential Equations with Space-Time Dependent Coefficients	2
Intermediate Variable Emulation: Using Internal Processes in Simulators to Build More Informative Emulators	2
Sensitivity Analysis of Quasi-Stationary Distributions (QSDs) of Mass-Action Systems	2
A Combination Technique for Optimal Control Problems Constrained by Random PDEs	2
Corrigendum: Quasi–Monte Carlo Finite Element Analysis for Wave Propagation in Heterogeneous Random Media	2
Proportional Marginal Effects for Global Sensitivity Analysis	2
Landmark-Warped Emulators for Models with Misaligned Functional Response	2
Are Minimizers of the Onsager–Machlup Functional Strong Posterior Modes?	2
Stacking Designs: Designing Multifidelity Computer Experiments with Target Predictive Accuracy	2
Leveraging Joint Sparsity in Hierarchical Bayesian Learning	2
Analysis of Nested Multilevel Monte Carlo Using Approximate Normal Random Variables	2
Gaussian Processes with Input Location Error and Applications to the Composite Parts Assembly Process	2
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	2
Active Learning of Tree Tensor Networks using Optimal Least Squares	2
Adaptive Multilevel Subset Simulation with Selective Refinement	1
Deep Neural Network Surrogates for Nonsmooth Quantities of Interest in Shape Uncertainty Quantification	1
Deep Learning in High Dimension: Neural Network Expression Rates for Analytic Functions in \(\pmb{L^2(\mathbb{R}^d,\gamma_d)}\)	1
Monte Carlo Methods for the Neutron Transport Equation	1
Discovering the Unknowns: A First Step	1
Conditional Sampling with Monotone GANs: From Generative Models to Likelihood-Free Inference	1
Towards Practical Large-Scale Randomized Iterative Least Squares Solvers through Uncertainty Quantification	1
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	1
Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	1
Conglomerate Multi-fidelity Gaussian Process Modeling, with Application to Heavy-Ion Collisions	1
Nonasymptotic Bounds for Suboptimal Importance Sampling	1

The Bayesian Approach to Inverse Robin Problems	1
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	1
A Stochastic Levenberg--Marquardt Method Using Random Models with Complexity Results	1
Certified Dimension Reduction for Bayesian Updating with the Cross-Entropy Method	1
Differential Equation–Constrained Optimization with Stochasticity	1
Covariance Expressions for Multifidelity Sampling with Multioutput, Multistatistic Estimators: Application to Approximate Control Variates	1
Rank Bounds for Approximating Gaussian Densities in the Tensor-Train Format	1
Multiple Closed Curve Modeling with Uncertainty Quantification for Shape Analysis	1
Data-Driven Rules for Multidimensional Reflection Problems	1
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors	1
Computationally Efficient Sampling Methods for Sparsity Promoting Hierarchical Bayesian Models	1