GANISP: a GAN-assisted Importance SPlitting Probability Estimator

TL;DR

GANISP leverages generative adversarial networks to produce system-consistent perturbations, enhancing variance reduction in importance splitting for rare event probability estimation in deterministic systems.

Contribution

This paper introduces GANISP, a novel GAN-assisted importance splitting method that improves rare event probability estimation in deterministic systems by generating attractor-consistent perturbations.

Findings

GANISP achieves better variance reduction than traditional methods.

The method is effective for deterministic systems with complex attractors.

Implementation is available in an open-source repository.

Abstract

Designing manufacturing processes with high yield and strong reliability relies on effective methods for rare event estimation. Genealogical importance splitting reduces the variance of rare event probability estimators by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is difficult when applied to deterministic systems where the initial conditions of the offspring realizations need to be modified. Typically, a random perturbation is applied to the offspring to differentiate their trajectory from the parent realization. However, this random perturbation strategy may be effective for some systems while failing for others, preventing variance reduction in the probability estimate. This work seeks to address this limitation using a generative model such as a Generative Adversarial Network (GAN) to generate perturbations that are consistent with the attractor of the dynamical system. The proposed GAN-assisted Importance SPlitting method (GANISP) improves the variance reduction for the system targeted. An implementation of the method is available in a companion repository (https://github.com/NREL/GANISP).