University of Notre Dame, USA
Simulation for Machine Learning and Machine Learning for Simulation in Thermal Science
Abstract: Simulation has been an integral part of thermal science, and machine learning is trending to be so as well. In this talk, I will introduce our efforts in utilizing computer simulations to produce data that are used for machine learning tasks for high-throughput property prediction and inverse materials design. On the other hand, some simulation techniques for modeling thermal phenomena at different scales exist but are computationally expensive. I will also talk about our results in leveraging machine learning techniques to help speed up such time-consuming thermal simulations, making them more practically useful for real systems.
Dr. Tengfei Luo is a Professor in the Department of Aerospace and Mechanical Engineering (AME) at the University of Notre Dame (UND). Before joining UND, he was a postdoctoral associate at Massachusetts Institute of Technology (2009-2011) after obtaining his PhD from Michigan State University (2009). Dr. Luo’s research focuses on exploring the chemistry-conformation-property relationships of polymers using molecular simulations, machine learning and experiments. He is an ASME Fellow (2019), JSPS Invitational Fellow (2019), DuPont Young Professor Awardee (2016), DARPA Young Faculty Awardee (2015), and Air Force Summer Faculty Fellow (2015).