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.

Zoom link: https://ncsu.zoom.us/j/97638681103?pwd=dDJrRkE3d3NQZEhrRlhOMDc4T0pRUT09