Extreme Event Likelihoods with Guided Generative Models

Across science, engineering, and finance, many of the most important risks come from low-likelihood, high-impact events. Estimating the probability of these events with brute-force Monte Carlo sampling—running a model repeatedly with randomly drawn inputs to estimate the probability of rare outcomes—can require an excessive volume of model iterations, especially when each sample comes from an expensive model. NVIDIA's research introduces guided generative models to more efficiently estimate the likelihood of extreme events, reducing computational cost while maintaining accuracy. This approach has potential applications in risk assessment across multiple domains including science, engineering, and finance.