Lambert TheisenRWTH Aachen University · Chair of Applied and Computational Mathematics
Lambert Theisen
Master of Science
Computational Engineer, PhD Student, Digital Creator @ RWTH Aachen University/University of Stuttgart → https://thsn.dev
About
8
Publications
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14
Citations
Introduction
→ See my personal webpage: https://thsn.dev
→ Computational Engineer, PhD Student, Digital Creator.
@ RWTH Aachen University / University of Stuttgart
# Researching PDE eigenvalue problems, asymptotic analysis of expanding domains, directional homogenization, preconditioners for eigenvalue algorithms, preconditioners for linear solvers, spectral coarse spaces for domain decomposition, and Galerkin methods for moment models in rarefied gas modelling.
Additional affiliations
October 2022 - present
October 2019 - May 2021
Position
- Research Assistant
Description
- See homepage http://www.mathcces.rwth-aachen.de/5people/theisen/start
September 2018 - October 2019
Position
- Research Assistant
Description
- Teaching for: - Mathematische Grundlagen IV (CES) @ MathCCES, RWTH Aachen - WS18/19: Partielle Differentialgleichungen (CES) @ MathCCES, RWTH Aachen
Education
October 2019 - June 2024
April 2018 - September 2019
October 2014 - April 2018
Publications
Publications (8)
We present a mixed finite element solver for the linearized regularized 13-moment equations of non-equilibrium gas dynamics. The Python implementation builds upon the software tools provided by the FEniCS computing platform. We describe a new tensorial approach utilizing the extension capabilities of FEniCS’ Unified Form Language to define required...
This paper provides a provably quasi-optimal preconditioning strategy of the linear Schrödinger eigenvalue problem with periodic potentials for a possibly non-uniform spatial expansion of the domain. The quasi-optimality is achieved by having the iterative eigenvalue algorithms converge in a constant number of iterations for different domain sizes....
Accelerating iterative eigenvalue algorithms is often achieved by employing a spectral shifting strategy. Unfortunately, improved shifting typically leads to a smaller eigenvalue for the resulting shifted operator, which in turn results in a high condition number of the underlying solution matrix, posing a major challenge for iterative linear solve...
ddEigenLab.jl: Domain-Decomposition Eigenvalue Problem Lab
Accelerating iterative eigenvalue algorithms is often achieved by employing a spectral shifting strategy. Unfortunately, improved shifting typically leads to a smaller eigenvalue for the resulting shifted operator, which in turn results in a high condition number of the underlying solution matrix, posing a major challenge for iterative linear solve...
This paper provides a provably optimal preconditioning strategy of the linear Schrödinger eigenvalue problem with periodic potentials for a possibly non-uniform spatial expansion of the domain. The optimality is achieved by having the iterative eigenvalue algorithms converge in a constant number of iterations with respect to different domain sizes....
We present a mixed finite element solver for the linearized R13 equations of non-equilibrium gas dynamics. The Python implementation builds upon the software tools provided by the FEniCS computing platform. We describe a new tensorial approach utilizing the extension capabilities of FEniCS's Unified Form Language (UFL) to define required differenti...
