Computational Mathematics and Statistics Seminar with Ming Zhong: How Scientific Machine Learning Can Be Used for Knowledge Discovery?
Speaker: Ming Zhong, assistant professor, Department of Applied Math, Illinois Institute of Technology
Title: How Scientific Machine Learning Can Be Used for Knowledge Discovery?
Abstract: Identifying the driving force for certain motion (i.e. planetary motion) or leading cause for certain disease (John Snow’s Cholera experiment in 1800 London) from data has been a crucial part of scientific development of human knowledge. As observation/sensing techniques has boomed in the recently years, how to make scientific discoveries from large dataset has become a great challenge.
We propose several scientific machine learning (a new machine learning paradigm) techniques as a modern mathematical tool for knowledge discovery. We discuss two major applications of SciML, one is to use knowledge based statistical learning to discover dynamical models for modeling self organization (clustering, flocking, swarming, etc.), the other is to use physics informed machine learning to solve nonlinear stiff and hyperbolic PDEs (Burgers, Allen Can, Euler Equations) related forward and backward problems. We will discuss both theoretical advancements and computational challenges in these applications.
Meeting ID: 889 6322 6743
Passcode: 536600
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Computational Mathematics and Statistics
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