A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations-Reference-Cited by-同舟云学术

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A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations

Published:2022-10 Issue: Volume:400 Page:115600
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Nitzler Jonas^ORCID,Biehler Jonas,Fehn Niklas^ORCID,Koutsourelakis Phaedon-Stelios,Wall Wolfgang A.^ORCID

Funder

Deutsche Forschungsgemeinschaft

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference59 articles.

1. Survey of multifidelity methods in uncertainty propagation, inference, and optimization;Peherstorfer;SIAM Rev.,2018

2. Multi-fidelity approaches for uncertainty quantification;Biehler;Surveys for Applied Mathematics and Mechanics (GAMM Mitteilungen),2019

3. Multilevel Monte Carlo methods;Heinrich,2001

4. Multilevel Monte Carlo path simulation;Giles;Oper. Res.,2008

5. Multilevel Monte Carlo methods;Giles;Acta Numer.,2015

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