Computational mathematics

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.

Theodoros Katsaounis

Professor, Department of Mathematics and Applied Mathematics, University of Crete (UoC)

Computational mathematics Mathematical modeling scientific computing numerical methods for PDE's

Dr. Theodoros research interests are in the area of applied and computational mathematics with emphasis on the development, analysis and implementation of numerical methods for approximating solutions of PDE’s modelling various physical processes.

Pierre-David Letourneau

Visiting Student, Waves in Complex Media

Computational mathematics waves in heterogeneous media fast algorithms

Visiting Student, April 2012 - June 2012 Introduction Pierre-David Letourneau is currently a PhD candidate at Stanford University. He visited us from April 2012 to June 2012 and worked on fast algorithms for strong-scattering large-scale systems, and imaging through clutter media. Research Interests Pierre-David's research interests lie in the area of applied and computational mathematics with topics including imaging, inverse problems, waves in heterogeneous media as well as fast algorithms. His work focuses on both the theoretical and computational aspects of wave propagation, and includes

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.

Ioanna Mousikou

Ph.D. Student, Applied Mathematics and Computational Science

Applied PDE's numerical methods for PDE's Computational mathematics

Short Bio Ms. Ioanna Mousikou is a Ph.D student in the AppliedPDE group of the Applied Mathematics and Computational Science program (AMCS) of CEMSE division at the King Abdullah University of Science and Technology, Saudi Arabia. During her second year at KAUST, she received a Master's degree in Applied Mathematics where the thesis was focused on numerical methods for hyperbolic conservation laws. Before joining KAUST, Ms. Ioanna completed undergraduate degree in Mathematics at the University of Cyprus. Education Profile M.Sc. KAUST, Saudi Arabia, 2016 B.Sc. Mathematics, University of Cyprus