Research Themes
You bring the data, the physics, and the application.
"We provide Computational AI Solutions with Mathematical Guarantees"
Most scientific machine learning works until it doesn't, and nobody can say in advance when it will be. My group builds the other kind: methods whose accuracy can be estimated, whose training can be made to converge, and whose predictions obey the physical laws of the system they model — by construction, not by penalty. The work spans approximation theory, training algorithms, and deployed surrogates for industrial CFD, plasma, and thermo-fluid systems, in collaboration with LG Electronics, LLNL, ORNL, and PNNL.
Mathematical Foundations of Scientific Machine Learning
Training, Optimization and Automated Discovery
Physics-Informed Learning and Neural Operator
Structure-Preserving Modeling for Dynamics and Digital Twins
Research Support & Funding
Our research is generously supported by the following organizations:
National Science Foundation (NSF) Computational Mathematics Program (DMS-2513966)
LG Electronics Energy Solution
Global Science Research Center (CM2LA) funded by the Ministry of Science and ICT of Korea (RS-2023-00219980)
Disclaimer: Any opinions, findings, and conclusions or recommendations expressed on this website are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funding agencies.