Title: A feature-capturing PINN for interface problems.
Presenter: Dr. Te-Sheng Lin,

Affiliation: National Yang Ming Chiao Tung University, Taiwan and University of Pennsylvania
Abstract: In this talk, we introduce feature-capturing physics-informed neural networks designed to solve fluid-structure interaction problems. We first reformulate the governing equations in each fluid domain separately and replace the singular force effect by the traction balance equation between solutions in two sides of the interface. Since the pressure is discontinuous and the velocity has discontinuous derivatives across the interface, we hereby design neural network functions to capture the pressure and velocity behavior across the interface sharply. Through a series of numerical experiments, the results indicate that the models employed in the current network can achieve high prediction accuracy, and the accuracy is comparable with traditional grid-based methods.