Heard on Campus: PhysicsX founder on deep learning in physics and engineering

UNIVERSITY PARK, Pa. — “Deep learning is driving a revolution in physics, science, and engineering,” said Robin Tuluie, founder and chairman of PhysicsX, during the Alumni and Friends Reunion of the Penn State Institute for Gravitation and the Cosmos (IGC) on May 30. Tuluie, who was a postdoctoral scholar with the IGC from 1993 to 1995, presented a talk about the use of deep learning to push the boundaries of engineering.

Deep learning is a type of artificial intelligence machine learning inspired by neural networks in the human brain, using several layers of artificial neurons to process data in different ways. Much like large language models (LLMs) — a type of deep learning model— like OpenAI’s ChatGPT and Microsoft Copilot “train” on existing texts and language to create logical sentences, Large physics models and large geometry models train on data from physics, geometry and spatial relationships in order to generate predictions for variables like air or fluid flow across arbitrary geometries. PhysicsX has created large physics models and large geometry models to address engineering challenges in optimizing structures, from aircraft wings and hydro-turbine blades to the tires of Formula 1 cars and the heat-exchangers used to keep batteries in electric vehicles cool.

Traditional analysis of structural optimization, Tuluie explained, might involve running many simulations to explore, for example, how adjusting aspects of an airplane wing’s geometry might reduce drag. But even on the fastest supercomputers, the hundreds of simulations required might take hours, days or even months to run. But after a day or two of training, the deep geometry models can accomplish the same results in less than a second.