OOIR: Observatory of International Research

Papers

(The TQCC of SIAM-ASA Journal on Uncertainty Quantification is 5. 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 2020-11-01 to 2024-11-01.)

Article	Citations
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark	121
A Quasi-Monte Carlo Method for Optimal Control Under Uncertainty	27
Cross-Entropy-Based Importance Sampling with Failure-Informed Dimension Reduction for Rare Event Simulation	22
Stochastic Normalizing Flows for Inverse Problems: A Markov Chains Viewpoint	20
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification	18
Fokker--Planck Particle Systems for Bayesian Inference: Computational Approaches	17
Computing Shapley Effects for Sensitivity Analysis	17
Parameter Estimation in an SPDE Model for Cell Repolarization	14
Multilevel Monte Carlo Finite Difference Methods for Fractional Conservation Laws with Random Data	14
Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions	13
Global Sensitivity Analysis and Wasserstein Spaces	11
Optimal Design of Large-scale Bayesian Linear Inverse Problems Under Reducible Model Uncertainty: Good to Know What You Don't Know	11
Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters	11
GAN-Based Priors for Quantifying Uncertainty in Supervised Learning	10
Uncertainty Quantification for the BGK Model of the Boltzmann Equation Using Multilevel Variance Reduced Monte Carlo Methods	10
A Spline Dimensional Decomposition for Uncertainty Quantification in High Dimensions	10
Generalized Sparse Bayesian Learning and Application to Image Reconstruction	9
Efficient Estimation of the ANOVA Mean Dimension, with an Application to Neural Net Classification	9
Emulation of Stochastic Simulators Using Generalized Lambda Models	9
PDE-Constrained Optimal Control Problems with Uncertain Parameters using SAGA	9
Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling	9
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design	9
Nonlinear Reduced Models for State and Parameter Estimation	9
Linked Gaussian Process Emulation for Systems of Computer Models Using Matérn Kernels and Adaptive Design	9
A Convex Optimization Framework for the Inverse Problem of Identifying a Random Parameter in a Stochastic Partial Differential Equation	8

Asymptotic Analysis of Multilevel Best Linear Unbiased Estimators	8
Quasi-Monte Carlo Finite Element Analysis for Wave Propagation in Heterogeneous Random Media	8
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems Using Deep Neural Networks	8
Quantifying Truncation-Related Uncertainties in Unsteady Fluid Dynamics Reduced Order Models	8
APIK: Active Physics-Informed Kriging Model with Partial Differential Equations	8
Convergence Rates for Learning Linear Operators from Noisy Data	8
On the Asymptotical Regularization for Linear Inverse Problems in Presence of White Noise	8
Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion	7
Density Estimation by Randomized Quasi-Monte Carlo	7
A Hybrid Gibbs Sampler for Edge-Preserving Tomographic Reconstruction with Uncertain View Angles	7
Scaled Vecchia Approximation for Fast Computer-Model Emulation	7
Empirical Bayesian Inference Using a Support Informed Prior	7
EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors	7
A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models	7
A Generalized Kernel Method for Global Sensitivity Analysis	6
Uncertainty Quantification of Inclusion Boundaries in the Context of X-Ray Tomography	6
Joint Online Parameter Estimation and Optimal Sensor Placement for the Partially Observed Stochastic Advection-Diffusion Equation	6
A Stochastic Levenberg--Marquardt Method Using Random Models with Complexity Results	6
Multilevel Ensemble Kalman–Bucy Filters	6
Representing Model Discrepancy in Bound-to-Bound Data Collaboration	6
Reproducing Kernel Hilbert Spaces, Polynomials, and the Classical Moment Problem	5
Analysis of Nested Multilevel Monte Carlo Using Approximate Normal Random Variables	5
Post hoc Uncertainty Quantification for Remote Sensing Observing Systems	5
Model Error Estimation Using the Expectation Maximization Algorithm and a Particle Flow Filter	5
Statistical Finite Elements via Langevin Dynamics	5
Can We Trust Bayesian Uncertainty Quantification from Gaussian Process Priors with Squared Exponential Covariance Kernel?	5
Optimization-Based Markov Chain Monte Carlo Methods for Nonlinear Hierarchical Statistical Inverse Problems	5
Instances of Computational Optimal Recovery: Dealing with Observation Errors	5
Deep Learning in High Dimension: Neural Network Expression Rates for Analytic Functions in \(\pmb{L^2(\mathbb{R}^d,\gamma_d)}\)	5
Quantifying and Managing Uncertainty in Piecewise-Deterministic Markov Processes	5
Ensemble Approximate Control Variate Estimators: Applications to MultiFidelity Importance Sampling	5