报告题目 (Title)：Towards Third Wave AI: Interpretable, Robust Trustworthy Machine Learning for Diverse Applications in Science and Engineering (迈向第三波人工智能：可解释、稳健、值得信赖的机器学习在科学和工程中的各种应用)

报告人 (Speaker)：林光 教授(Purdue University，美国)

报告时间 (Time)：2023年11月13日10:00

报告地点 (Place)：腾讯会议 207-598-084

邀请人(Inviter)：李常品、蔡敏

主办部门：理学院数学系

报告摘要：This talk aims to close the gap by developing new theories and scalable numerical algorithms for complex dynamical systems that can be realistically predicted and validated. We are creating new technologies that can be translated into more secure and reliable new trustworthy AI systems that can be deployed for real-time complex dynamical system prediction, surveillance, and defense applications to improve the stability and efficiency of complex dynamical systems and national security of the United States. We will introduce new NNs that learn functionals and nonlinear operators from functions with simultaneous uncertainty estimates. We present a series of multi-fidelity, federated, Bayesian neural operator network architectures in scientific machine learning. In addition, we will discuss how to incorporate Physics Knowledge and AI to design new interpretable models for science and engineering. In particular, we will present two data-science cases: (1) predicting the COVID-19 pandemic with uncertainties using trustworthy data-driven epidemiological models; (2) Data-driven causal model discovery and personalized prediction in Alzheimer’s disease.