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

Dec 20, 2024 Today, Yuhuang Meng has joined our group as a PhD candidate. He will work on machine learning-enhanced numerical solvers and is co-supervised by Jing Zhao.
Dec 17, 2024 On December 17, 2024, I gave a keynote presentation on Geometric Challenges in Machine Learning-Based Surrogate Models at the CASML 2024 conference at IISc Bangalore in India. The slides can be found here.
Jul 1, 2024 This summer, I will be visiting the Chinese University of Hong Kong to collaborate with Prof. Jun Zou.

recent publications

  1. CMAME
    Overlapping Schwarz preconditioners for randomized neural networks with domain decomposition
    Yong Shang, Alexander HeinleinSiddhartha Mishra, and 1 more author
    Computer Methods in Applied Mechanics and Engineering, 2025
  2. MathOptML
    Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems
    Alexander HeinleinAmanda A. HowardDamien Beecroft, and 1 more author
    In Mathematical Optimization for Machine Learning, 2025
    Section: Mathematical Optimization for Machine Learning
  3. IEEE Access
    DDU-Net: A Domain Decomposition-Based CNN for High-Resolution Image Segmentation on Multiple GPUs
    Corné Verburg, Alexander Heinlein, and Eric C. Cyr
    IEEE Access, 2025
  4. SISC
    Algebraic Construction of Adaptive Coarse Spaces for Two-Level Schwarz Preconditioners
    Alexander Heinlein, and Kathrin Smetana
    SIAM Journal on Scientific Computing, 2025
  5. MATHMOD2025
    Towards Model Discovery Using Domain Decomposition and PINNs
    Tirtho S. Saha, Alexander Heinlein, and Cordula Reisch
    IFAC-PapersOnLine, 2025
    11th Vienna International Conference on Mathematical Modelling MATHMOD 2025
  6. JCOMP
    Learning the solution operator of two-dimensional incompressible Navier-Stokes equations using physics-aware convolutional neural networks
    Viktor GrimmAlexander Heinlein, and Axel Klawonn
    Apr 2025
    Accepted for publication in the Journal of Computational Physics
  7. Numerical study on hyper parameter settings for neural network approximation to partial differential equations
    Hee Jun Yang, Alexander Heinlein, and Hyea Hyun Kim
    Mar 2025
    Submitted
  8. Trilinos: Enabling Scientific Computing Across Diverse Hardware Architectures at Scale
    Matthias MayrAlexander Heinlein, Christian Glusa, and 30 more authors
    Mar 2025
    Submitted
  9. Towards Graph Neural Network Surrogates Leveraging Mechanistic Expert Knowledge for Pandemic Response
    Agatha Schmidt, Henrik Zunker, Alexander Heinlein, and 1 more author
    Apr 2025