Alberto F. Martín

Computational Science and Engineering. High Performance Scientific Computing. Partial Differential Equations. Finite Elements. Parallel Preconditioning for Large Scale PDEs. Scalable Preconditioning. Scientific Machine Learning. Artificial Neural Networks for PDEs. Open Source Scientific Software. GPU computing. Exploiting the synergy among Artificial Neural Networks and the numerical solution of PDEs. Scientific Machine Learning approches for large-scale inverse problems. Physics-informed Neural Networks with application to prediction of bushfire dynamics. Hybridizable finite element methods for general polytopal meshes (HDG, HHO, VEM, etc.). Scalable preconditioning for these schemes (h/p geometric multigrid approaches, hybrid multiscale domain decomposition, etc.). Numerical methods for geophysical flows with application to Numerical Weather Prediction.