报告时间：2024年12月18日 15:00开始
报 告 人：毛志平教授
报告地点：9-218
报告题目：Deep neural networks learning and Deep neural operator learning for complex fluid with partial or scarce data
报告摘要：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 but also the scientific computing. In this talk, I will introduce my work using physics-informed neural networks (PINNs) and neural operator learning-based deep multi-scale multi-physics nets (DeepMMnet) for complex fluid with partial or scarce data. 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 deep neural operator networks (DeepOnets).
报告人简介：毛志平教授2009年本科毕业于重庆大学，2015年博士毕业于厦门大学计算数学专业，2015年10 月至 2020 年9 月在美国布朗大学应用数学系从事博士后研究，国家级青年人才计划入选者。毛志平教授主要从事深度学习与偏微分方程数值解，特别是谱方法研究以及深度学习求解复杂系统方面的研究，其目前在SIREV, JCP, SISC, SINUM, CMAME等国际高水平杂志上发表论文30 余篇。
