Speaker: Prof. Ling Guo (Shanghai Normal University)

Title: Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations

Time: 2022.06.10, Friday 9:00—10:00 am (Beijing)

Tencent ID: 251835114

Abstract:

In this talk, we will introduce a sampling based machine learning approach, Monte Carlo physics informed neural networks (MC-PINNs), for solving forward and inverse fractional partial differential equations (FPDEs). As a generalization of physics informed neural networks (PINNs), MC-PINNs method relies on deep neural network surrogates in addition to a stochastic approximation strategy for computing the fractional derivatives of the DNN outputs, which can yield less overall computational cost compared to fPINNs and thus provide an opportunity for solving high dimensional fractional PDEs. We demonstrate the performance of MC-PINNs method via several numerical examples.

Biography:

Ling Guo is a Professor in the Department of Mathematic at Shanghai Normal University. She received her Ph.D. (2007) from Shanghai Jiao Tong University. She joined Shanghai Normal University as an assistant Professor in 2007. She was an associate professor from 2013 to 2018, visiting scholar at Purdue University from 2010 to 2011, and visiting scholar at Brown University in 2016. Her research interests include stochastic modeling, uncertainty quantification and scientific machine leaning.