S&DS Seminar: Lu Lu

In-Person seminars will be held at Mason Lab 211 with optional remote access:
(https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa)

Physics-informed deep learning: Blending data and physics for learning functions and operators

Abstract: Deep learning has achieved remarkable success in diverse applications; however, its use in scientific applications has emerged only recently. In this talk, I will first review physics-informed neural networks (PINNs) and available extensions for solving forward and inverse problems of partial differential equations (PDEs). I will then introduce a less known but powerful result that a NN can accurately approximate any nonlinear operator. This universal approximation theorem of operators is suggestive of the potential of NNs in learning operators of complex systems. I will present the deep operator network (DeepONet) to learn various operators that represent deterministic and stochastic differential equations. I will demonstrate the effectiveness of DeepONet and its extensions to diverse multiphysics and multiscale problems, such as nanoscale heat transport, bubble growth dynamics, high-speed boundary layers, electroconvection, hypersonics, and geological carbon sequestration. Deep learning models are usually limited to interpolation scenarios, and I will quantify the extrapolation complexity and develop a complete workflow to address the challenge of extrapolation for deep neural operators.

Bio: I am an Assistant Professor in Department of Chemical and Biomolecular Engineering at University of Pennsylvania. My current research interest is on scientific machine learning. My broad research interests focus on multiscale modeling and high performance computing. Website: https://lululxvi.github.io/