DIAM, Faculty of EEMCS
Mekelweg 4, 2628 CD Delft
Room HB 03.290
Tel.: +31 (0)15 27 89135
Alexander Heinlein is assistant professor in the Numerical Analysis group of the Delft Institute of Applied Mathematics (DIAM), Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS), at the Delft University of Technology (TU Delft).
His main research areas are numerical methods for partial differential equations and scientific computing, in particular, solvers and discretizations based on domain decomposition and multiscale approaches. He is interested in high-performance computing (HPC) and solving challenging problems involving, e.g., complex geometries, highly heterogenous coefficient functions, or the coupling of multiple physics. More recently, Alexander also started focusing on the combination of scientific computing and machine learning, a new research area also known as scientific machine learning (SciML). Generally, his work includes the development of new methods and their theoretical foundation as well as their implementation on current computer architectures (CPUs, GPUs) and application to real world problems.
|Jun 30, 2023||The European Trilinos User Group Meeting 2023 took place on the campus of the Delft University of Technology. Please see the blog post for more information.|
|Apr 11, 2023||The European Trilinos User Group Meeting 2023 will take place on the campus of the Delft University of Technology. I will co-organize the meeting together with Matthias Mayr. Please see eurotug.github.io for more information.|
|Oct 5, 2022||New open master projects on Improving Nonlinear Solver Convergence Using Machine Learning (co-supervised by Tycho van Noorden (COMSOL)) and Domain Decomposition for Machine Learning Based Medical Imaging (co-supervised by Eric Cyr (Sandia National Laboratories)) available; see master thesis projects.|
- IPDPSAn Experimental Study of Two-level Schwarz Domain-Decomposition Preconditioners on GPUsIn 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2023
- JCOMPTowards parallel time-stepping for the numerical simulation of atherosclerotic plaque growthJournal of Computational Physics, 2023
- CMAMA Multi-Level Extension of the GDSW Overlapping Schwarz PreconditionerComputational Methods in Applied Mathematics, 2023
- Learning the solution operator of two-dimensional incompressible Navier-Stokes equations using physics-aware convolutional neural networksAug 2023Submitted
- A Comparison Of Direct Solvers In FROSch Applied To Chemo-MechanicsJul 2023Submitted
- A computational framework for pharmaco-mechanical interactions in arterial walls using parallel monolithic domain decomposition methodsJul 2023Submitted
- Multilevel domain decomposition-based architectures for physics-informed neural networksJun 2023Submitted