Computer Science Researchers Receive U.S. Department of Energy Grant
Image: Comparison of a physical model computed by a physics code (red dots) with that predicted by a Random Forest Regression model (green line) shows the ML-based RFR model does a good job of predicting code outputs in this particular case. The overall project focuses on methods for automating the generation of ML-based tools and workflows in computational science problems. Image source: doi:10.1017/S0022377822000708 (2022).
Wes Bethel, associate professor of Computer Science, is the principal investigator of a research project that will study methods for automating the generation of software tools and processes for the purpose of constructing software that builds machine learning models.
The work is in support of an effort funded by U.S. Department of Energy’s Office of Fusion Energy Sciences to leverage artificial intelligence (AI)/machine learning tools to reduce computational time-to-solution for specific physics calculations, with the ultimate objective of being able to predict plasma behavior in real time in fusion tokamak devices.
Bethel, who joined SF State in 2022 after a career as a computer scientist at Lawrence Berkeley National Laboratory, has assembled a team of Computer Science graduates and undergraduates (V. Cramer, C. Pestano, A. del Rio and S. Verma), and other faculty (Computer Science Lecturer Lothar Narins) to study this problem. The SF State team is part of a larger multi-institutional effort led by J. Wright at the Massachusetts Institute of Technology and includes researchers from the Princeton Plasma Physics Laboratory and Lawrence Berkeley National Laboratory.
More information: SFSU's Campus Memo.
Contact: Prof. E. Wes Bethel, CS Dept., ewbethel@sfsu.edu
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