Alexander Heinlein


TU Delft

DIAM, Faculty of EEMCS

Numerical Analysis

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 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.

recent publications

  1. IPDPS
    An Experimental Study of Two-level Schwarz Domain-Decomposition Preconditioners on GPUs
    Ichitaro Yamazaki, Alexander Heinlein, and Sivasankaran Rajamanickam
    In 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2023
  2. JCOMP
    Towards parallel time-stepping for the numerical simulation of atherosclerotic plaque growth
    Stefan Frei, and Alexander Heinlein
    Journal of Computational Physics, 2023
  3. CMAM
    A Multi-Level Extension of the GDSW Overlapping Schwarz Preconditioner
    Alexander HeinleinOliver Rheinbach, and Friederike Röver
    Computational Methods in Applied Mathematics, 2023
  4. Learning the solution operator of two-dimensional incompressible Navier-Stokes equations using physics-aware convolutional neural networks
    Viktor GrimmAlexander Heinlein, and Axel Klawonn
    Aug 2023
  5. A Comparison Of Direct Solvers In FROSch Applied To Chemo-Mechanics
    Alexander Heinlein, Björn Kiefer, Stefan Prüger, and 2 more authors
    Jul 2023
  6. A computational framework for pharmaco-mechanical interactions in arterial walls using parallel monolithic domain decomposition methods
    Daniel BalzaniAlexander HeinleinAxel Klawonn, and 5 more authors
    Jul 2023
  7. Multilevel domain decomposition-based architectures for physics-informed neural networks
    Victorita DoleanAlexander HeinleinSiddhartha Mishra, and 1 more author
    Jun 2023