Alexander Heinlein

prof_pic5.jpg

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.

news

May 14, 2024 During the coming weeks, I will give keynote presentations at the HPCSE24 conference in the Czech Republic and the Preconditioning 2024 conference in the USA.
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.

recent publications

  1. SISC
    Algebraic construction of adaptive coarse spaces for two-level Schwarz preconditioners
    Alexander Heinlein, and Kathrin Smetana
    Nov 2024
    Accepted for publication in the SIAM Journal on Scientific Computing
  2. IMAJNA
    An extension of the approximate component mode synthesis method to the heterogeneous Helmholtz equation
    Elena GiammatteoAlexander Heinlein, and Matthias Schlottbom
    Oct 2024
    Accepted for publication in the IMA Journal of Numerical Analysis
  3. Towards Graph Neural Network Surrogates Leveraging Mechanistic Expert Knowledge for Pandemic Response
    Agatha Schmidt, Henrik Zunker, Alexander Heinlein, and 1 more author
    Nov 2024
  4. High-order discretized ACMS method for the simulation of finite-size two-dimensional photonic crystals
    Elena GiammatteoAlexander Heinlein, Philip L. Lederer, and 1 more author
    Oct 2024
    Submitted
  5. Towards Model Discovery Using Domain Decomposition and PINNs
    Tirtho S. Saha, Alexander Heinlein, and Cordula Reisch
    Oct 2024
    Submitted
  6. Domain decomposition method with randomized neural networks
    Yong Shang, Alexander HeinleinSiddhartha Mishra, and 1 more author
    Aug 2024
    Submitted
  7. Nonlinear Two-Level Schwarz Methods: A Parallel Implementation in FROSch
    Alexander Heinlein, Kyrill Ho, Axel Klawonn, and 1 more author
    Aug 2024
    Submitted
  8. Two-level deep domain decomposition method
    Victorita Dolean, Serge Gratton, Alexander Heinlein, and 1 more author
    Aug 2024
    Submitted
  9. Coupling deal.II and FROSch: A Sustainable and Accessible (O)RAS Preconditioner
    Sebastian Kinnewig, Alexander Heinlein, and Thomas Wick
    Aug 2024
    Submitted
  10. A computational study of algebraic coarse spaces for two-level overlapping additive Schwarz preconditioners
    Filipe A. C. S. Alves, Alexander Heinlein, and Hadi Hajibeygi
    Aug 2024
    Submitted
  11. Coarse Spaces Based on Higher-Order Interpolation for Schwarz Preconditioners for Helmholtz Problems
    Erik Sieburgh, Alexander Heinlein, Vandana Dwarka, and 1 more author
    Aug 2024
    Submitted
  12. DDU-Net: A Domain Decomposition-based CNN for High-Resolution Image Segmentation on Multiple GPUs
    Corné Verburg, Alexander Heinlein, and Eric C. Cyr
    Jul 2024
    Submitted