Upcoming Event: Center for Scientific Machine Learning

Scientific Machine Learning for Differential Equations

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Abstract

Program Information:

Scientific Machine Learning (SciML) is an emerging research area focused on the opportunities, challenges and the interplay between machine learning and scientific computations with applications across science, engineering, and medicine. The third annual workshop on Scientific Machine Learning, hosted by Dr. Tan Bui-Thanh, Dr. Stella Offner, Krishnanunni C.G., and with the help of Karen Rumph will focus on SciML approaches for forward, inverse, UQ, and generative modeling governed by differential equations. The workshop will feature talks by experts spanning computational science and engineering and data-driven machine learning to foster collaboration and establish central challenges and research directions in SciML for differential equations.

Important information: The third SciML workshop is takes place between the Artificial Intelligence and Digital Twins for Earth System Workshop (September 22-24, 2025), and the SIAM TX-LA Sectional meeting  (afternoon of September 26 and full day September 28, 2025). All three events are taking place in the Peter O'Donnell Building. 

Registration

Submission of talk

Submission of poster

Biography

Speakers:

Youngjoon Hong, Seoul National University 

Shinhoo Kang, Korea University Sejong

Albert Cerrone, University of Notre Dame

Yuehaw Khoo, University of Chicago

Felix J. Hermann, Georgia Tech

Xun Huan, University of Michigan 

Rebekah White, Sandia National Laboratories

Niall Mangan, ESAM, Northwestern University 

Gianluca Laccarino, Stanford University

Jeff Calder, University of Minnesota 

Thomas O'Leary Roseberry, Ohio State University 

Viviana Acquaviva, CUNY NYC College of Technology

Blake Bordelon, Harvard Applied Mathematics