»ã±¨±êÌâ (Title)£ºTowards Third Wave AI: Interpretable, Robust Trustworthy Machine Learning for Diverse Applications in Science and Engineering (ÂõÏòµÚÈý²¨ÈËΪÖÇÄÜ£º¿ÉÚ¹ÊÍ¡¢ÎÈÖØ¡¢ÖµµÃÐÅÈεĻúе½ø½¨ÔÚ¿ÆѧºÍ¹¤³ÌÖеĸ÷ÀàÀûÓÃ)

»ã±¨ÈË (Speaker)£ºÁֹ⠽ÌÊÚ(Purdue University

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»ã±¨¹¦·ò (Time)£º2023Äê11ÔÂ13ÈÕ10:00

»ã±¨µØÖ· (Place)£ºÌÚѶ»áÒé 207-598-084

Ô¼ÇëÈË(Inviter)£ºÀƷ¡¢²ÌÃô

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»ã±¨ÌáÒª£ºThis 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.