Adaptive reduced basis strategy for rare events simulations

Abstract : Monte-Carlo methods are well suited to characterize events of which associated probabilities are not too low with respect to the simulation budget. For very seldom observed events, these approaches do not lead to accurate results. Indeed, the number of samples are often insufficient to estimate such low probabilities (at least 10 +2 samples are needed to estimate a probability of 10 − with 10% relative deviation of the Monte-Carlo estimator). Even within the framework of reduced order methods, such as a reduced basis approach, it seems difficult to accurately predict low probability events. In this paper we propose to combine a cross-entropy method with a reduced basis algorithm to compute rare events (failure) probabilities.
Document type :
Journal articles
Complete list of metadatas

Cited literature [51 references]  Display  Hide  Download

https://hal-univ-paris10.archives-ouvertes.fr/hal-02332401
Contributor : Administrateur Hal Nanterre <>
Submitted on : Thursday, October 24, 2019 - 5:30:57 PM
Last modification on : Saturday, October 26, 2019 - 1:53:41 AM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2019-12-04

Please log in to resquest access to the document

Identifiers

Collections

Citation

L. Gallimard. Adaptive reduced basis strategy for rare events simulations. International Journal for Numerical Methods in Engineering, Wiley, 2019, 120 (3), pp.283-302. ⟨10.1002/nme.6135⟩. ⟨hal-02332401⟩

Share

Metrics

Record views

12