Title: Deep learning for high dimensional PDEs

Speaker: Tao Zhou(Chinese Academy of Sciences)

Abstract: We present a deep adaptive sampling method for solving PDEs where deep neural networks are utilized to approximate the solutions. More precisely, we propose the failure informed adaptive sampling for PINNs and an adaptive important sampling scheme for deep Ritz. Both approaches can adaptively refine the training set with the goal of reducing the failure probability. Applications to both forward and inverse PDEs problems will be presented.

Zoom: https://us06web.zoom.us/j/4564461054?pwd=TVUt6ys0rcjoVTMNAkaUmN8YJUiK8T.1&omn=82500360025

ID: 456 446 1054

PW: POSTECH11