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 12, 2022 I am co-organizing two mini-symposia at the 7th International Domain Decomposition Conference (DD27) in Prague, July 25-29, 2022: MS4: Spectral Coarse Spaces in Domain Decomposition Methods and Multiscale Discretizations together with Vitorita Dolean Maini (University of Strathclyde and Cote d’Azur University) and Kathrin Semtana (Stevens Institute of Technology) and MS9: Learning Algorithms, Domain Decomposition Methods, and Applications together with Xiao-Chuan Cai (University of Macau) and Axel Klawonn (University of Cologne).
Jun 9, 2022 I will give an invited plenary lecture on Robust, algebraic, and scalable Schwarz preconditioners with extension-based coarse spaces at the 7th International Domain Decomposition Conference (DD27) in Prague, July 25-29, 2022.
Jun 9, 2022 At the 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2022), Oslo, June 5 - 9, I gave an introductory lecture on Parallel Schwarz domain decomposition preconditioning and an introduction to FROSch; see the slides and the accompanying GitHub repository with the demo code.

recent publications (full list)

  1. SISC
    Adaptive GDSW Coarse Spaces of Reduced Dimension for Overlapping Schwarz Methods
    SIAM Journal on Scientific Computing. 2022
  2. SISC
    FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica
    Heinlein, Alexander, Perego, Mauro, and Rajamanickam, Sivasankaran
    SIAM Journal on Scientific Computing. 2022
  3. ETNA
    Surrogate convolutional neural network models for steady computational fluid dynamics simulations
    Eichinger, Matthias, Heinlein, Alexander, and Klawonn, Axel
    Electronic Transactions on Numerical Analysis (ETNA). 2022
  4. SISC
    Parallel Scalability of Three-Level FROSch Preconditioners to 220 000 Cores using the Theta Supercomputer
    Heinlein, Alexander, Rheinbach, Oliver, and Röver, Friederike
    Accepted for publication in SIAM Journal on Scientific Computing. April 2022
  5. SISC
    Adaptive nonlinear domain decomposition methods
    Heinlein, Alexander, Klawonn, Axel, and Lanser, Martin
    Accepted for publication in SIAM Journal on Scientific Computing. March 2022
  6. Towards parallel time-stepping for the numerical simulation of atherosclerotic plaque growth
    Frei, Stefan, and Heinlein, Alexander
    Submitted March 2022
  7. Comparison of Arterial Wall Models in Fluid-Structure Interaction Simulations
    Submitted March 2022