Time: 10:30-11:30 , Mar.25
Location: Tencent Meeting，Meeting ID：455-743-574，Link：https://meeting.tencent.com/dm/uOg229xpTe7C
Host: Qifeng Liao
Recently, neural network-based deep learning methods, which are different from the classical numerical methods, have attracted lots of attention not only in the traditional artificial intelligence community but also the scientific computing community. In this talk, I will introduce my work using physics-informed neural networks (PINNs) and deep multi-scale multi-physics nets (DeepMMnet) for high-speed flows. In particular, I shall solve the inverse problems of the shock wave problems in supersonic flow by using PINNs based on the information of density gradient ?ρ and limited data of pressure and inflow conditions instead of using boundary conditions. Then I will introduce the inference of the flow past a normal shock in hypersonic flow by using the DeepMMnets with the help of DeepOnets.
Dr. Zhiping Mao is currently a Professor in School of Mathematical Science Xiamen University. He got his PhD degree in 2015 from Xiamen University. He then went to in Division of Applied Mathematics, Brown University to worked as Postdoc. He mainly interested in spectral methods and deep learning, and he published more then 20 SCI papers on top journal such as SIREV, SINUM, JCP, SISC, CMAME.