OOIR: Observatory of International Research

Papers

(The TQCC of SIAM-ASA Journal on Uncertainty Quantification is 4. 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 2020-05-01 to 2024-05-01.)

Article	Citations
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark	106
A Quasi-Monte Carlo Method for Optimal Control Under Uncertainty	21
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification	18
Cross-Entropy-Based Importance Sampling with Failure-Informed Dimension Reduction for Rare Event Simulation	17
Fokker--Planck Particle Systems for Bayesian Inference: Computational Approaches	15
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint	13
Parameter Estimation in an SPDE Model for Cell Repolarization	12
Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions	12
Computing Shapley Effects for Sensitivity Analysis	12
Multilevel Monte Carlo Finite Difference Methods for Fractional Conservation Laws with Random Data	11
Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters	9
Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling	9
GAN-Based Priors for Quantifying Uncertainty in Supervised Learning	9
Optimal Design of Large-scale Bayesian Linear Inverse Problems Under Reducible Model Uncertainty: Good to Know What You Don't Know	9
Quantifying Truncation-Related Uncertainties in Unsteady Fluid Dynamics Reduced Order Models	8
On the Asymptotical Regularization for Linear Inverse Problems in Presence of White Noise	8
Emulation of Stochastic Simulators Using Generalized Lambda Models	8
A Spline Dimensional Decomposition for Uncertainty Quantification in High Dimensions	8
Uncertainty Quantification for the BGK Model of the Boltzmann Equation Using Multilevel Variance Reduced Monte Carlo Methods	8
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	7
Density Estimation by Randomized Quasi-Monte Carlo	7
Efficient Estimation of the ANOVA Mean Dimension, with an Application to Neural Net Classification	7
Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design	7
Global Sensitivity Analysis and Wasserstein Spaces	7
Nonlinear Reduced Models for State and Parameter Estimation	7

Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion	7
Quasi-Monte Carlo Finite Element Analysis for Wave Propagation in Heterogeneous Random Media	7
PDE-Constrained Optimal Control Problems with Uncertain Parameters using SAGA	7
A Hybrid Gibbs Sampler for Edge-Preserving Tomographic Reconstruction with Uncertain View Angles	6
Generalized Sparse Bayesian Learning and Application to Image Reconstruction	6
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks	6
EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors	6
Asymptotic Analysis of Multilevel Best Linear Unbiased Estimators	6
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	6
Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems	5
Model Error Estimation Using the Expectation Maximization Algorithm and a Particle Flow Filter	5
Multilevel Ensemble Kalman–Bucy Filters	5
Can We Trust Bayesian Uncertainty Quantification from Gaussian Process Priors with Squared Exponential Covariance Kernel?	5
Instances of Computational Optimal Recovery: Dealing with Observation Errors	5
Representing Model Discrepancy in Bound-to-Bound Data Collaboration	5
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	5
A Convex Optimization Framework for the Inverse Problem of Identifying a Random Parameter in a Stochastic Partial Differential Equation	5
Scaled Vecchia Approximation for Fast Computer-Model Emulation	5
Ensemble Approximate Control Variate Estimators: Applications to MultiFidelity Importance Sampling	4
Estimation of Ordinary Differential Equation Models with Discretization Error Quantification	4
Bayesian Inference of an Uncertain Generalized Diffusion Operator	4
Analysis of Nested Multilevel Monte Carlo Using Approximate Normal Random Variables	4
Asymptotic Theory of \(\boldsymbol \ell _1\) -Regularized PDE Identification from a Single Noisy Trajectory	4
Reproducing Kernel Hilbert Spaces, Polynomials, and the Classical Moment Problem	4
Two-Level a Posteriori Error Estimation for Adaptive Multilevel Stochastic Galerkin Finite Element Method	4
Numerical Approximation of Optimal Convergence for Fractional Elliptic Equations with Additive Fractional Gaussian Noise	4
A Generalized Kernel Method for Global Sensitivity Analysis	4
Joint Online Parameter Estimation and Optimal Sensor Placement for the Partially Observed Stochastic Advection-Diffusion Equation	4
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	4
Post hoc Uncertainty Quantification for Remote Sensing Observing Systems	4