AM Seminar: Discovering governing equations from data using sparse identification and deep learning methods

Speaker: 

Joseph Bakarji, Postdoctoral Fellow, AI Institute in Dynamic Systems, University of Washington

Description:

 Recent advancements in machine learning have revolutionized computational solvers and enabled data-driven discovery in complex systems. However, combining available data with known physical laws to maximize
generalization and guarantee robustness remains a significant challenge. I will present our latest work that addresses this challenge using deep learning and sparse identification methods. First, I will demonstrate how
probability distribution function equations can be inferred from Monte Carlo simulations for the purpose of coarse-graining and closure modeling. Second, I will present three methods for discovering dimensionless groups from data using the Buckingham Pi theorem as a constraint. Third, I will discuss the deep delay autoencoder algorithm, which reconstructs high-dimensional systems of differential equations from partial measurements, as motivated by Takens' theorem. Finally, I will highlight the limitations of these methods and suggest potential avenues for future
research.

Dial-In Information

https://ucsc.zoom.us/j/93888058985?pwd=WVVqTXk0ZEIvd3kwZXF0VWdzZ1F5Zz09

Wednesday, February 1, 2023 at 12:00pm to 1:00pm

Engineering 2, E2 506
Engineering 2 1156 High Street, Santa Cruz, California 95064

Event Type

Lectures & Presentations

Invited Audience

Alumni, Faculty & Staff, Students, Graduate Students

Topics

Science & Technology

Group
Applied Math Department, Baskin School of Engineering
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