ResearchTrend.AI
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis

Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis

Qiao Chen
Elise Arnaud
Ricardo Baptista
O. Zahm
ArXivPDFHTML

Papers citing "Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis"

3 / 3 papers shown
Title
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities
Sharp detection of low-dimensional structure in probability measures via dimensional logarithmic Sobolev inequalities
Matthew T.C. Li
Tiangang Cui
Fengyi Li
Youssef Marzouk
O. Zahm
14
4
0
18 Jun 2024
Operator Learning: Algorithms and Analysis
Operator Learning: Algorithms and Analysis
Nikola B. Kovachki
S. Lanthaler
Andrew M. Stuart
36
22
0
24 Feb 2024
Sensitivity analysis for multidimensional and functional outputs
Sensitivity analysis for multidimensional and functional outputs
Fabrice Gamboa
Alexandre Janon
T. Klein
A. Lagnoux
30
112
0
07 Nov 2013