14 citations · 30 across the 12 of their papers we have counts for
12 papers · 1 filter
Multifidelity Covariance Estimation via Regression on the Manifold of Symmetric Positive Definite Matrices
Aimee Maurais, Terrence Alsup, Benjamin Peherstorfer +1
We introduce a multifidelity estimator of covariance matrices formulated as the solution to a regression problem on the manifold of symmetric positive definite matrices. The estima…
Multilevel Monte Carlo estimators for derivative-free optimization under uncertainty
Friedrich Menhorn, Gianluca Geraci, D. Thomas Seidl +3
Optimization is a key tool for scientific and engineering applications, however, in the presence of models affected by uncertainty, the optimization formulation needs to be extende…
Principal Feature Detection via -Sobolev Inequalities
Matthew T. C. Li, Youssef Marzouk, Olivier Zahm
We investigate the approximation of high-dimensional target measures as low-dimensional updates of a dominating reference measure. This approximation class replaces the associated…
Cross-entropy-based importance sampling with failure-informed dimension reduction for rare event simulation
Felipe Uribe, Iason Papaioannou, Youssef M. Marzouk +1
The estimation of rare event or failure probabilities in high dimensions is of interest in many areas of science and technology. We consider problems where the rare event is expres…
Low-rank multi-parametric covariance identification
Antoni Musolas, Estelle Massart, Julien M. Hendrickx +2
We propose a differential geometric construction for families of low-rank covariance matrices, via interpolation on low-rank matrix manifolds. In contrast with standard parametric…
Data-Driven Forward Discretizations for Bayesian Inversion
Daniele Bigoni, Yuming Chen, Nicolas Garcia Trillos +2
This paper suggests a framework for the learning of discretizations of expensive forward models in Bayesian inverse problems. The main idea is to incorporate the parameters governi…