Monte-Carlo acceleration: importance sampling and hybrid dynamic systems

TL;DR

This paper introduces an importance sampling method tailored for multi-component hybrid dynamical systems modeled as PDMPs, significantly improving the efficiency of rare event probability estimation in complex industrial systems.

Contribution

It adapts importance sampling to piecewise deterministic Markov processes, enabling efficient rare event simulation in systems with deterministic and stochastic dynamics.

Findings

Significant reduction in computational cost compared to crude Monte-Carlo

Effective biasing strategy for importance sampling in PDMPs

Successful application to a three-component system simulation

Abstract

The reliability of a complex industrial system can rarely be assessed analytically. As system failure is often a rare event, crude Monte-Carlo methods are prohibitively expensive from a computational point of view. In order to reduce computation times, variance reduction methods such as importance sampling can be used. We propose an adaptation of this method for a class of multi-component dynamical systems. We address a system whose failure corresponds to a physical variable of the system (temperature, pressure, water level) entering a critical region. Such systems are common in hydraulic and nuclear industry. In these systems, the statuses of the components (on, off, or out-of-order) determine the dynamics of the physical variables, and is altered both by deterministic feedback mechanisms and random failures or repairs. In order to deal with this interplay between components status and physical variables we model trajectory using piecewise deterministic Markovian processes (PDMP). We show how to adapt the importance sampling method to PDMP, by introducing a reference measure on the trajectory space, and we present a biasing strategy for importance sampling. A simulation study compares our importance sampling method to the crude Monte-Carlo method for a three-component-system.