  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).
