Scientific Machine Learning
Abstract
The main objective of this course is to teach concepts and implementation of deep learning techniques for scientific and engineering problems. This course entails various methods, including the theory and implementation of deep learning techniques to solve a broad range of computational problems frequently encountered in solid mechanics, fluid mechanics, nondestructive evaluation of materials, systems biology, chemistry, non-linear dynamics, etc.
Instructor: Lu Lu, Yale University
Lu Lu is an Assistant Professor in the Departments of Statistics and Data Science and of Chemical and Environmental Engineering at Yale University. Prior to joining Yale, he was an Assistant Professor in the Department of Chemical and Biomolecular Engineering at University of Pennsylvania from 2021 to 2023, and an Applied Mathematics Instructor in the Department of Mathematics at Massachusetts Institute of Technology from 2020 to 2021. He obtained his Ph.D. degree in Applied Mathematics at Brown University in 2020, master's degrees in Engineering, Applied Mathematics, and Computer Science at Brown University, and bachelor's degrees in Mechanical Engineering, Economics, and Computer Science at Tsinghua University in 2013. His current research interest lies in scientific machine learning and artificial intelligence for science, including theory, algorithms, software, and its applications to engineering, physical, and biological problems. His broad research interests focus on multiscale modeling and high performance computing for physical and biological systems. He has received the MIT Technology Review Innovators under 35 Asia Pacific, U.S. Department of Energy Early Career Award, Mathematics Young Investigator Award from MDPI, and Joukowsky Family Foundation Outstanding Dissertation Award of Brown University.
Format
- Date: Monday, June 21, 2027
- Location: University of Connecticut, Storrs (specific room TBA)
- Duration: Full day (approximately 9:00 a.m. – 5:00 p.m.)
- Includes: Coffee breaks and lunch
Fees and Registration
The fee and registration details will be announced soon. Enrollment is subject to capacity.