# Lambert Theisen

M.Sc.
→ 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.
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## Research Interest & Projects

### Journal Publications

A Quasi-Optimal Factorization Preconditioner for Periodic Schrödinger Eigenstates in Anisotropically Expanding Domains
SIAM Journal on Numerical Analysis Vol. 60, Iss. 5 (2022)
09/2022Benjamin StammLambert Theisen

This paper provides a provably quasi-optimal preconditioning strategy of the linear Schrödinger eigenvalue problem with periodic potentials for a possibly nonuniform 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. In the analysis, we derive an analytic factorization of the spectrum and asymptotically describe it using concepts from the homogenization theory. This decomposition allows us to express the eigenpair as an easy-to-calculate cell problem solution combined with an asymptotically vanishing remainder. We then prove that the easy-to-calculate limit eigenvalue can be used in a shift-and-invert preconditioning strategy to bound the number of eigensolver iterations uniformly. Several numerical examples illustrate the effectiveness of this quasi-optimal preconditioning strategy.

Keywords: periodic Schrödinger equation, iterative eigenvalue solvers, preconditioner, asymptotic eigenvalue analysis, factorization principle, directional homogenization
fenicsR13: A Tensorial Mixed Finite Element Solver for theLinear R13 Equations Using the FEniCS Computing Platform
ACM Trans. Math. Softw. 47, 2, Article 17 (April 2021)
04/2021Lambert TheisenManuel Torrilhon

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 differential operators for tensors above second degree. The presented solver serves as an example for implementing tensorial variational formulations in FEniCS, for which the documentation and literature seem to be very sparse. Using the software abstraction levels provided by the Unified Form Language allows an almost one-to-one correspondence between the underlying mathematics and the resulting source code. Test cases support the correctness of the proposed method using validation with exact solutions. To justify the usage of extended gas flow models, we discuss typical application cases involving rarefaction effects. We provide the documented and validated solver publicly.

Keywords: tensorial mixed finite element method, R13 equations, FEniCS project, continuous interior penalty

### Preprints

→ Currently none.

### Talks

[TODO Upload PDFs at uniform location, indicate invited and contributed talks, make first page as preview clickable.]

### Software

• DBPrices.jl: A Julia wrapper for the db-prices module from @juliuste.
• ddEigenlab.jl: A Domain-Decomposition Eigenvalue Problem Lab to benchmark various algorithms.
• fenicsR13: A Tensorial Mixed Finite Element Solver for the Linear R13 Equations Using the FEniCS Computing Platform.
• latextools: Some scripts to process LaTeX documents. Contains a conversion script for LaTeX files into plaintext while keeping the mathematical sentence structure intact. For example, the input Let $$$h = \tfrac{1}{N}$$$ where $N$ denotes the number of intervals. will be converted to Let noun verbs noun where noun denotes the number of intervals. (valid sentence).

### Miscellaneous

• Generate pictures of arbitrary-order Lagrangian finite element basis function with the Mathematica script from 10.6084/m9.figshare.9767021.v1 (uploaded to figshare).

### Theses and Supervised Work

• Density Operator in Eigenvalue Problems with Application in Manifold Interpolation, Bachelor Thesis of Stefan Berger, RWTH Aachen, 2022.
• Evaluation and Implementation of Schrödinger-Type Eigenvalue Problems in Long Rectangular Domains using the Finite Element Method, CES Project Thesis of Corinna Müller, Matthias Geratz, Celine Heger, Johanna Meyer, RWTH Aachen, 2021.
• Using a Spectral Inference Network to Solve the Time-Independent Schrödinger Equation for a Two-Dimensional Hydrogen Atom, Seminar Thesis of Alexander Kristof, RWTH Aachen, 2020.
• Iterative Domain Decomposition Methods for Eigenvalue Problems, Master Thesis of Hendrik Borchardt, RWTH Aachen, 2020.
• Simulation of Non-Equilibrium Gas Flows Using the FEniCS Computing Platform, Master Thesis of Lambert Theisen, RWTH Aachen / MathCCES, 2020.
• Shear-Slip Mesh Update Method for Compressible Flow Simulations Involving Rotating Sub-Domains, Seminar Thesis of Lambert Theisen, RWTH Aachen / CATS, 2019.
• Automated Boundary Layer Mesh Generation for Simulation of Convective Cooling, Bachelor Thesis of Lambert Theisen, RWTH Aachen / ABB Switzerland, 2018.

## Teaching

You can find most of my teaching activity in the Github repository @lamBOOO/teaching.

### Selected classes

Higher mathematics 1 for engineers [global exercise]
Höhere Mathematik 1 für Ingenieure [Vortragsübung]
WS221000University of Stuttgart

Linear algebra is the study of the basic concepts and techniques involving vectors and matrices. The course topics include logic, numbers and sets; vectors and vector spaces; systems of linear equations; linear transformations and their properties; eigenvalues and eigenvectors. The course objectives are to develop students’ skills in reasoning, modeling and problem-solving with vectors and matrices.

#8
Mathematical aspects in computational chemistry [global exercise]
Mathematische Aspekte in der computergestützten Chemie [Vortragsübung]
SS2210RWTH Aachen University

This course explores the use of mathematical concepts in computational chemistry, specifically in creating and breaking down models of molecules. We will take a mathematical approach to theoretical chemistry, covering topics such as electric charge interactions between molecular systems, the transition from classical to quantum mechanics, the Hartree-Fock model, and its breakdown. If time allows, we will also examine some aspects of Density Functional Theory (DFT). By the end of the course, students will have a deep understanding of the mathematical principles underlying computational chemistry and will be able to apply these principles to their own research in the field.

#7
Foundations of Mathematics III [global exercise]
Mathematische Grundlagen III (CES) [Vortragsübung]
WS2170RWTH Aachen University

This course introduces variational calculus, which is a branch of mathematics that deals with finding the best solution to a problem involving functions. It also teaches how to integrate functions in several variables and on different types of spaces, such as curves and surfaces. The course also covers numerical methods for solving ordinary differential equations, which are equations that relate a function and its derivatives. Moreover, the course explores optimization techniques for finding the minimum or maximum value of a function and eigenvalue computation methods for finding the characteristic values of a matrix. The course aims to help students acquire and apply these mathematical tools in various fields of science and engineering.

#6
Foundations of Mathematics III [global exercise]
Mathematische Grundlagen III (CES) [Vortragsübung]
WS2070RWTH Aachen University

This course introduces variational calculus, which is a branch of mathematics that deals with finding the best solution to a problem involving functions. It also teaches how to integrate functions in several variables and on different types of spaces, such as curves and surfaces. The course also covers numerical methods for solving ordinary differential equations, which are equations that relate a function and its derivatives. Moreover, the course explores optimization techniques for finding the minimum or maximum value of a function and eigenvalue computation methods for finding the characteristic values of a matrix. The course aims to help students acquire and apply these mathematical tools in various fields of science and engineering.

#5
Foundations of Mathematics IV [global exercise]
Mathematische Grundlagen IV (CES) [Vortragsübung]
SS2070RWTH Aachen University

In this course, students will explore the theory and numerics of partial differential equations (PDEs), which are mathematical models of phenomena involving rates of change in multiple variables. The course will cover various aspects of PDEs, such as their classification by type and basic characteristics, their elementary solution methods for some classical examples, their generalization by using distributions and Sobolev spaces to define weak derivatives, their analysis by applying Fourier and other integral transformations to different domains, their discretization by finite difference methods on grids, and their numerical solution by efficient techniques such as FFT or filtering. The course will combine theoretical lectures with practical exercises using MATLAB or Julia.

#4
Foundations of Mathematics I [global exercise]
Mathematische Grundlagen I (CES) [Vortragsübung]
WS1970RWTH Aachen University

In this course, you will learn about various aspects of linear algebra and analysis of functions of several variables. You will explore how to solve eigenvalue problems and transform matrices into diagonal or normal forms. You will also learn how to use singular value decomposition, rank determination and regularization concepts. You will apply differentiation, Taylor expansion, inverse and implicit functions to analyze and optimize multivariable functions. You will use iterative methods such as Newton’s method or Gauss-Newton method to solve nonlinear systems of equations and least squares problems. You will understand how to interpolate data using polynomials and how to perform numerical differentiation and integration using Newton-Cotes formulas, Gauss quadrature and extrapolation. Finally, you will get an introduction to the theory of ordinary differential equations.

#3
Foundations of Mathematics IV [tutor]
Mathematische Grundlagen IV (CES) [Tutor]
SS1950RWTH Aachen University

I executed the self exercise and supervised students in the course on partial differential equations (PDEs). I learned and applied the theory and numerics of PDEs, such as their types, characteristics, solutions, generalizations, analysis, discretization and numerical solution. I also helped the students understand and practice these concepts and methods using MATLAB or Julia.

#2
Partial differential equations (CES) [tutor]
Partielle Differentialgleichungen (CES) [Tutor]
WS1850RWTH Aachen University

This course covers various aspects of the variational formulation for elliptic problems, such as the Galerkin technique and the Lax-Milgram theorem. It also introduces the finite element method for elliptic problems and some modern iterative methods, such as PCG and multigrid method. The course then extends to parabolic problems and shows how to use the method of lines for their discretization. It also presents the finite volume method as another discretization technique. The course then deals with saddle point problems and their application to Stokes equations. Finally, it discusses the Navier-Stokes equation for incompressible fluids. The main goals of this course are to help students understand the basic principles of discretizing partial differential equations and to teach them how to use different numerical methods for solving them. The students will also learn how to evaluate the results of these methods and how to adapt them to new tasks. The students will acquire confidence in using discretization techniques such as finite elements and finite volume methods, as well as iterative solution methods such as PCG and multigrid method.

#1
[TODO: Add PDFs previews of notes]

## Selected Projects

• The tool gradescaler.com provides a graphical overview of exam grading schemes.
• Use the shopping-list LaTeX template to create your 4x4 double-sided reusable shopping list for easy ticking of required products based on the supermarket location to optimize shopping. Here's an example.
• A while ago, I build the website kanara-bau.de without using any tech stack in the editor.

## Contact

### E-Mail

You can contact me directly via the e-mail address Maillmbrt∂thsn.dev or Maillambert.theisen∂rwth-aachen.de preferrable using PGP encryption. My PGP key can be found on the keyserver keys.openpgp.org or you can directlty download it here using the link lt-pgpkey.asc. The corresponding signature reads 9C32 B2D9 E59B 09C1 72AB C577 F2C2 52C0 F331 EB87.

### Profiles

I am part of the major science and networking sites, for example:

Also have a look at my institute webpages:

### Impressum

Applied and Computational Mathematics (ACoM)
RWTH Aachen University
Schinkelstr. 2, Room 229 (Rogowski Building, 2nd floor)
52062 Aachen
Germany
Office Phone: 0049 241 80-98671
Mobile Phone: 0049 241 80-98686

Institut für Angewandte Analysis und Numerische Simulation (IANS)
Lehrstuhl Numerische Mathematik für Höchstleistungsrechner (NMH)
Universität Stuttgart
Pfaffenwaldring 57, Raum 7.154
70569 Stuttgart
Germany
Office Phone: 0049 711 685 65522

[TODO Add small CV section with logos and timeline.][TODO Add better email obfuscate strategy.]