题目：Physics-Informed Neural Network and its Applications to Digital Twin
汇报人：Rong Pan
讲座时间：2023年5月11日（周四）15:00-16:00
地点：综合楼644会议室
报告人简介：
Dr. Rong Pan is an associate professor of Industrial Engineering in the School of Computing and Augmented Intelligence at Arizona State University. His research interests include failure time data analysis, design of experiments, multivariate statistical process control, time series analysis, and computational Bayesian methods. His research has been supported by NSF, Arizona Science Foundation, Air Force Research Lab, etc. He has published over 90 journal papers and 50+ refereed conference papers. Dr. Pan is a senior member of ASQ, IIE, and IEEE, and a lifetime member of SRE. He serves on the editorial boards of Journal of Quality Technology and Quality Engineering.
摘要：
Dynamics of physical phenomena are modeled by ordinary differential equations and partial differential equations, where the solutions are typically not easy to find. A recent trend is to solve these equations by some data-driven approaches through machine learning, such as Gaussian processes or neural networks. Physics-informed neural networks (PINNs) are a type of neural networks that incorporate physical laws or principles into their architectures, thus forcing the neural network prediction to abide physical laws. In this talk, I will discuss a 3D printing process called direct ink writing (DIW) and how PINNs can be used for predicting the fluid flow inside printer nozzle, which directly affects the quality of the 3D printing process. I will discuss several neural network architectures and compare their performance.
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