Guillaume Chennetier — Statistician and PhD candidate

Journal article

G. Chennetier, H. Chraibi, A. Dutfoy, J. Garnier
SIAM Journal of Uncertainty Quantification, 2023

Cite

APA Click to copy
Chennetier, G., Chraibi, H., Dutfoy, A., & Garnier, J. (2023). Adaptive importance sampling based on fault tree analysis for piecewise deterministic Markov process. SIAM Journal of Uncertainty Quantification.

Chicago/Turabian Click to copy
Chennetier, G., H. Chraibi, A. Dutfoy, and J. Garnier. “Adaptive Importance Sampling Based on Fault Tree Analysis for Piecewise Deterministic Markov Process.” SIAM Journal of Uncertainty Quantification (2023).

MLA Click to copy
Chennetier, G., et al. “Adaptive Importance Sampling Based on Fault Tree Analysis for Piecewise Deterministic Markov Process.” SIAM Journal of Uncertainty Quantification, 2023.

BibTeX Click to copy

@article{g2023a,
  title = {Adaptive importance sampling based on fault tree analysis for piecewise deterministic Markov process},
  year = {2023},
  journal = {SIAM Journal of Uncertainty Quantification},
  author = {Chennetier, G. and Chraibi, H. and Dutfoy, A. and Garnier, J.}
}

Abstract

Piecewise deterministic Markov processes (PDMPs) can be used to model complex dynamical industrial systems. The counterpart of this modeling capability is their simulation cost, which makes reliability assessment untractable with standard Monte Carlo methods. A significant variance reduction can be obtained with an adaptive importance sampling (AIS) method based on a cross-entropy (CE) procedure. The success of this method relies on the selection of a good family of approximations of the committor function of the PDMP. In this paper original families are proposed. They are well adapted to high-dimensional industrial systems. Their forms are based on reliability concepts related to fault tree analysis: minimal path sets and minimal cut sets. The proposed method is discussed in detail and applied to academic systems and to a realistic system from the nuclear industry.