GANISP: a GAN-assisted Importance SPlitting Probability Estimator
This work addresses a specific bottleneck in manufacturing process design for reliability, offering an incremental improvement over existing perturbation strategies.
The paper tackles the challenge of variance reduction in rare event probability estimation for deterministic systems by introducing GANISP, a method that uses a Generative Adversarial Network to generate perturbations consistent with the system's attractor, improving variance reduction for targeted systems.
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).