digital twins

AI-based hydrogen plant models improve power grid stability

1 min read · Wed, Mar 18 2026

Green Hydrogen energy storage cyber-physical systems digital twins

Realistic AI models of hydrogen plants help stabilize power grids with high levels of renewable energy.

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.

kaust-cemse-ece-ccsl-zhandos-zhakipov

Zhandos Zhakipov

Ph.D. Student, Electrical and Computer Engineering

5G and beyond wireless communication networks digital twins