Scientific Machine Learning

Rigorous Model-Constrained Scientific Machine Learning for Digital Twins: A Computational Mathematics Perspective

Tan Bui-Thanh, Professor, Endowed William J. Murray, Jr. Fellow in Engineering No. 4, Oden Institute for Computational Engineering & Sciences, Department of Aerospace Engineering & Engineering Mechanics, The University of Texas at Austin (UT Austin)

Feb 26, 14:30 - 15:30

B1 L3 R3119

Scientific Machine Learning SciML Scientific Deep Learning SciDL deep learning machine learning digital twins uncertainty quantification Computational mathematics

This talk will outline a principled pathway from traditional computational mathematics to rigorously grounded Scientific Machine Learning (SciML) and present recent Scientific Deep Learning (SciDL) methods for forward modeling, inverse and calibration problems, and uncertainty quantification, emphasizing mathematical structure, stability, and generalization.

Empowering Natural Intelligence with Artificial Intelligence: a Mathematician's Perspective

Alfio Quarteroni, Emeritus Professor, Politecnico di Milano and EPFL

Jan 25, 14:00 - 15:00

B9, L2, R2322

Computational mathematics numerical methods Scientific Machine Learning scientific computing applied mathematics

A Dean’s Distinguished Lecture on natural intelligence, artificial intelligence and scientific machine learning.

Jongho Park

Research Scientist, Applied Mathematics and Computational Science

Computational mathematics numerical analysis mathematical optimization machine learning Scientific Machine Learning computational imaging

Jongho Park is a Research Scientist at King Abdullah University of Science and Technology (KAUST) where his research focuses on the design and analysis of efficient numerical methods for variational problems.