Towards Scientific AI for the simulation and optimization of complex systems

Raphael Pestourie

Assistant Professor, Georgia Tech, School of Computational Science and Engineering

Abstract

Complex systems are hard to simulate and even more difficult to optimize. In this talk, I will showcase how surrogate models accelerate the evaluation of properties of solutions to partial differential equations. I will present a precise definition of the computational benefit of surrogate models and example surrogate models. We will then show how surrogate models can be combined to solve a challenging multiscale problem in optics. We will show that, through a synergistic combination of data-driven methods and direct numerical simulations, surrogate-based models present a data-efficient and physics-enhanced approach to simulating and optimizing complex systems. This approach has the benefit of being interpretable. I will also share ways forward and opportunities for synergistical collaborations.

Bio

Pestourie is an assistant professor at Georgia Tech in the School of Computational Science and Engineering. He earned his PhD in applied mathematics from Harvard University and pursued his postdoctoral studies in the mathematics department at MIT. His research is dedicated to developing computational methodologies that leverage both data and scientific knowledge to systematically find solutions to engineering problems.

Live Webinar

Web ID: 932 8776 6557