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 heterogeneous 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.


Jan 28, 2024 I have been invited to speak in the CRUNCH seminar CRUNCH Group, Division of Applied Mathematics, Brown University. My talk on Domain decomposition for physics-informed neural networks is scheduled for March 22nd. Here, you can find the slides and video recording.
Dec 27, 2023 I have been featured in the GAMM Rundbrief with a description of my research.
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.

recent publications

  1. GAMM-Mitteilungen
    A computational framework for pharmaco-mechanical interactions in arterial walls using parallel monolithic domain decomposition methods
    Daniel BalzaniAlexander HeinleinAxel Klawonn, and 5 more authors
    GAMM-Mitteilungen, Nov 2024
  2. Coupled2023
    A Comparison Of Direct Solvers In FROSch Applied To Chemo-Mechanics
    Alexander Heinlein, Björn Kiefer, Stefan Prüger, and 2 more authors
    In 10th edition of the International Conference on Computational Methods for Coupled Problems in Science and Engineering, Nov 2023
  3. Ocean Engineering
    Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data
    Svenja Ehlers, Marco Klein, Alexander Heinlein, and 4 more authors
    Ocean Engineering, Nov 2023
  4. Multifidelity domain decomposition-based physics-informed neural networks for time-dependent problems
    Alexander HeinleinAmanda A. HowardDamien Beecroft, and 1 more author
    Jan 2024
  5. Improving Pseudo-Time Stepping Convergence for CFD Simulations With Neural Networks
    Anouk Zandbergen, Tycho van Noorden, and Alexander Heinlein
    Oct 2023
  6. 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