Lu Lu

Lu Lu

, University of Pennsylvania
Title: Physics-informed deep learning: Blending data and physics for learning functions and operators
Monday, February 27, 2023 4:00PM to 5:00PM
Mason Lab 211 See map
9 Hillhouse Ave
New Haven, CT 06511
Website
Information and 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.

Lu Lu is an Assistant Professor in the Department of Chemical and Biomolecular Engineering at University of Pennsylvania. He is also a faculty of Graduate Group in Applied Mathematics and Computational Science and of Penn Institute for Computational Science. Prior to joining Penn, he was an Applied Mathematics Instructor in the Department of Mathematics at Massachusetts Institute of Technology from 2020 to 2021. He obtained his Ph.D. degree in Applied Mathematics at Brown University in 2020, master’s degrees in Engineering, Applied Mathematics, and Computer Science at Brown University, and bachelor’s degrees in Mechanical Engineering, Economics, and Computer Science at Tsinghua University in 2013. The goal of his research is to model and simulate physical and biological systems at different scales by integrating modeling, simulation, and machine learning, and to provide strategies for system learning, prediction, optimization, and decision making in real time. His current research interest lies in scientific machine learning, including theory, algorithms, software, and its applications to engineering, physical, and biological problems. His broad research interests focus on multiscale modeling and high performance computing for physical and biological systems. Lu has received the 2022 U.S. Department of Energy Early Career Award, and 2020 Joukowsky Family Foundation Outstanding Dissertation Award of Brown University.
 

In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with the option of virtual participation (https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa)

3:30pm -   Pre-talk meet and greet teatime - Dana House, 24 Hillhouse Avenue