Malik Hassanaly, National Renewable Energy Laboratory (NREL)
Numerican Analysis Seminar
Title: Generative models and scientific sampling
Realistic image generation has benefited from recent groundbreaking advances in the ML community. At the core of generative models lies the capability to sample high-dimensional distributions with relatively small support and a priori unknown shape. In many scientific applications, this capability is a limiting factor that has led to modeling choices that circumvent the sampling problem. In this talk, I will demonstrate how generative models can tackle outstanding scientific challenges by leveraging their ability to sample high-dimensional distributions, or directly estimate them. While generative models are typically understood as data augmentation tools, their ability to handle high-dimensional distributions makes them also suited for data reduction. The sampling capabilities of generative models are illustrated for two scientific problems: atmospheric state inference, and turbulent combustion closure modeling.