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


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


 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

Dial-In Information

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


Science & Technology

Applied Math Department, Baskin School of Engineering
Google Calendar iCal Outlook

Recent Activity