hierarchical and sparse approximation

Darcy flow in random porous medium

AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches

Random PDEs stochastic algorithms Monte carlo methods Quasi-Monte Carlo Hierarchical regression Multilevel Monte Carlo Stochastic collocation Multi-index Low-rank approximation hierarchical and sparse approximation Bayesian Inversion Bayesian optimal experimental design

A course on modern numerical methods for random partial differential equations

Raul Tempone

Professor, Applied Mathematics and Computational Science

numerical analysis Computational finance computational statistics stochastic differential equations uncertainty quantification bayesian inference data assimilation hierarchical and sparse approximation optimal control optimal experimental design stochastic optimization

Professor Raul Tempone is an worlwide recognized researcher in numerical analysis and uncertainty quantification. Professor Raul Tempone at KAUST leads the stochastic numerics research group, significantly contributing to Saudi Arabia's Vision 2030 goals through advancements in computational science. His work in adaptive algorithms, Bayesian inverse problems, and scientific machine learning drives forward critical applications in technology and sustainability, embodying KAUST's commitment to global scientific leadership and economic diversification.