
Dr. A. (Alexander) Heinlein
Dr. A. (Alexander) Heinlein
Profile
Expertise
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.
Expertise
Publications
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2022
FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica
Alexander Heinlein / Mauro Perego / Sivasankaran Rajamanickam
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2021
Choosing the subregions in three-level frosch preconditioners
Alexander Heinlein / Oliver Rheinbach / Friederike Röver
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2021
Combining machine learning and domain decomposition methods for the solution of partial differential equations—A review
Alexander Heinlein / Axel Klawonn / Martin Lanser / Janine Weber
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2021
Estimating the time-dependent contact rate of sir and seir models in mathematical epidemiology using physics-informed neural networks
Viktor Grimm / Alexander Heinlein / Axel Klawonn / Martin Lanser / Janine Weber
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2021
Fully Algebraic Two-Level Overlapping Schwarz Preconditioners for Elasticity Problems
Alexander Heinlein / Christian Hochmuth / Axel Klawonn
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Ancillary activities
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2021-04-01 - 2023-04-01
Consultancy / research and other