讲座简介
Surrogate models have been widely used in advanced design and manufacturing to tackle the high computational cost in high-fidelity simulation and digital twin. Due to sensing errors, actuating errors, and computational errors, uncertainties inevitably exist in engineering systems. With incorporating the influence of uncertainties, stochastic surrogate modeling has become an emerging field. We developed two stochastic surrogates: (1) Neural Process Aided Ordinary Differential Equation (NP-ODE); (2) Neural network Gaussian process considering input uncertainty. We show the relationships between deep neural networks, Gaussian process, and differential equations, and use their relationships to develop new physics-informed data analytics methods. We also demonstrate their applications in engineering simulations such as finite element simulation and materials science simulation.
讲座人简介
Dr. Xiaowei Yueis an assistant professor at the Department of Industrial and Systems Engineering, Virginia Tech. He got his Ph.D. degree in industrial engineering, M.S. in Statistics from Georgia Tech. His research interests focus on engineering-driven data analytics for advanced manufacturing. His research has obtained more than 15 best paper awards. He is a recipient of SME Outstanding Young Manufacturing Engineer award, IISE Manufacturing & Design Outstanding Young Investigator Award, and Grainger Frontiers of Engineering Grant Award from the National Academy of Engineering. Dr. Yue serves as an associate editor for theIISE Transactionsand theJournal of Intelligent Manufacturing.
会议主题：Prof. Yue's Talk
会议时间：2022/11/30 10:00-11:30
腾讯会议链接：https://meeting.tencent.com/dm/z4lTeM1s71Gs#
腾讯会议号：452-866-421
