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.


Jan 17, 2022 New open master project, co-supervised by Edo Frederix (NRG) and Deepesh Toshniwal (TU Delft, Numerical Analysis), available; see thesis projects.
Dec 20, 2021 New open master project, co-supervised by Carolin Mehlmann (Max-Planck-Institute of Meteorology), available; see thesis projects.
Nov 20, 2021 Presentation about FROSch preconditioners for land ice simulations of Greenland and Antarctica at the Trilinos User-Developer Group Meeting 2021.

recent publications (full list)

  1. PAMM
    On temporal homogenization in the numerical simulation of atherosclerotic plaque growth
    Frei, Stefan, Heinlein, Alexander, and Richter, Thomas
    In PAMM 2021
  2. SISC
    FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica
    Heinlein, Alexander, Perego, Mauro, and Rajamanickam, Sivasankaran
    Accepted for publication in SIAM Journal on Scientific Computing. January 2022
  3. ETNA
    Surrogate Convolutional Neural Network Models for Steady Computational Fluid Dynamics Simulations
    Eichinger, Matthias, Heinlein, Alexander, and Klawonn, Axel
    Accepted for publication in ETNA. October 2021.
  4. SISC
    Adaptive GDSW coarse spaces of reduced dimension for overlapping Schwarz methods
    Accepted for publication in SIAM Journal on Scientific Computing. October 2021