That’s me

Researcher and team leader in the working group Computational Methods of Systems and Control Theory at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg and a Junior Professor at the Otto-von-Guericke University.

Interests
  • Differential-algebraic Equations
  • Systems and Control Theory
  • Flow Control
Education

  • PhD in Mathematics, 2014

    TU Berlin

Curriculum Vitae

 
 
 
 
 
Professor
Friedrich-Alexander University
Oct 2021 – Oct 2022 Erlangen
Teaching and Research in Mathematics of Data Science
 
 
 
 
 
Junior Professor
Otto-von-Guericke University
May 2018 – Present Magdeburg
Teaching and academic self-management
 
 
 
 
 
Researcher
MPI for Dynamics of Complex Technical Systems
Nov 2013 – Present Magdeburg
Basic research on control systems and responsibility for the research team Computer Aided Control System Design
 
 
 
 
 
Researcher
TU Berlin
Oct 2009 – Oct 2013 Berlin
PhD Projection on Decoupling, Semi-Discretization, and Optimal Control of Semi-linear Semi-explicit Index-2 Abstract Differential-Algebraic Equations and Application in Optimal Flow Control
 
 
 
 
 
Student Employee
Bombardier Transportation
Jun 2007 – Sep 2009 Berlin
Department of aerodynamics: implementation of an automated approach for shape optimization and wind tunnel testings

Teaching

Current Course (Summer Term 2021)

Topic Location
Lecture: Einführung in die Numerik 🎤 Mo 15-17 im G02-111 und Do 15-17 im G22A-105 LSF, Moodle
Exercise: Einführung in die Numerik 🎤 Fr 13-15 im G02-112 LSF, Moodle

Selected Previous Courses

Topic Occasion
Lecture: Einführung in die Mathematische Datenanalyse Summer Term 2022 at FAU StudOn, Skript
Lecture: Mathematik für Data Science 1&2 / Physik A&B Summer&Winter Term at FAU StudOn, Skript
Lecture: Mathematik 2 für Informatik Summer terms 2020 and 2021 at the OVGU
Seminar: Geometric formulations of inviscid fluids and their discretization (with C. Lessig) Winter term 2019 at OVGU LSF
Lecture: Differential-algebraic Equations: an introduction to the theoretical and numerical treatment of DAEs Winter terms 2018, 2019, 2020 at OVGU Script
Paperhive
Short course: Introduction to Tensor Based Computing MathCoRe Compact Course 2018
Lecture: Funktionentheorie (Complex Analysis) Winter term 2017 at OVGU
Lecture: Differential-algebraic Equations Summer term 2016 at OVGU
Short course: Model Reduction for Line Time Invariant Systems Summer 2015 at Shanghai University Slides

Recent Posts

Convolutional autoencoders for very low-dimensional parametrizations of nonlinear fluid flow
How to approximate general nonlinear systems by parameter dependent linear system using deep neural networks.
Feb 12, 2023 5 min read
Convolutional autoencoders for very low-dimensional parametrizations of nonlinear fluid flow
Pandoc vs. Pandemic
While things were shifting away from away from keyboard, I found little pleasures in seeing software tools in production. Most of all I enjoy small scripts1 that take a few lines of readable text and turn it into a website for an online course, into lecture notes, or a slide deck for presentation.
Apr 18, 2021 2 min read
Pandoc vs. Pandemic
Trends in Covid-19 Spread
How the daily changes in the cases may indicate whether the virus is on the decline.
Mar 22, 2020 7 min read
Trends in Covid-19 Spread
FEniCS Burgers POD DMD
A short python implementation of POD and DMD for a 2D Burgers equation using FEniCS and Scipy.
Mar 7, 2020 10 min read
FEniCS Burgers POD DMD
Adjoint Systems for Optimal Control from Pontryagin to Wikipedia
While it seems common knowledge how to formulate necessary optimality conditions backed by the famous Pontryagin’s Maximum Principle, the direct derivation from the very general original formulations is not obvious. Sometimes, authors like me1 provide some plausibility like obviously this term has to vanish, refer to other sources that miss the derivation too, or attach the complete book by Pontryagin to a specific formula2.
Aug 6, 2019 6 min read

Projects

.js-id-grants
All BA/MA PhD Software Industry Research Grants
MaRDI
The one mathematics directed and driven consortium in the NFDI. Our institute has been a co-applicant and we are co-managing (together with WWU Münster) the part on Scientific Computing (Task Area 2 in MaRDI).
mardi4nfdi.de Newsletter
MaRDI
Research Training Group: MathCoRe
The research training group MathCoRe at that OVGU provides a both challenging and supporting environment for a small community of graduate students to pursue a PhD in a field that relates to mathematical complexity reduction.
Home Apply
Research Training Group: MathCoRe
MRI Fingerprinting with Philips
on Improving Fingerprinting in MRI scans
Job Ad
MRI Fingerprinting with Philips
Open Master Thesis
on DMD models for flow problems based on time series using adaptive grids
Open Master Thesis
Inverse Triple Pendulum
An inverted triple pendulum as a test bench and demonstrator for control routines.
Inverse Triple Pendulum
Open Master Thesis
on Modelling of the impact of multiple scattering on measurements using luminescent particles
Open Master Thesis
dolfin-navier-scipy
A Python package that provides an interface between scipy and FEniCS in view of solving Navier-Stokes Equations.
Github Documentation DOI PyPI
dolfin-navier-scipy
hinf-lqgbt-nse
Python module for application of (Hinf-)LQG-balanced truncation for low-order controllers for the stabilization of Navier-Stokes equations
Github Documentation
hinf-lqgbt-nse
Open Theses Topics
Open projects available in different fields
Multiple Scattering in TPIV
Bachelor thesis on Modelling of the impact of multiple scattering on scalar measurements using luminescent particles
Multiple Scattering in TPIV

Conferences

Workshops&Visits

2023 – Meet me at the following occasions:

Date Occasion Location
Jan 16 Extended tutorial talk for the BIMoS days in January TU Berlin but online
Jan 27 – April 07 Research stay with Prof. Dawei Ding at the USTB Beijing Beijing, China
April 19–22 Representing MaRDI at the NFDI@MPG Workshop Ringberg
May 03–05 Annual Colloquium of MathCoRe Lutherstadt Wittenberg
May 17 Seminar talk on Reproducibility in Computer-based Experiments online by the Swiss Reproducibility Network
May 22–26 Workshop and conference on Nonlinear Model Reduction for Control Blacksburg, USA
May 30 – June 02 GAMM annual meeting Dresden
June 03 Long Night of Science, Magdeburg Magdeburg
July 24–26 SIAM Conference on Control and Its Applications Philadelphia, USA

See previous conferences and visits

My most recent talks

Computational Approaches to H-Infinity-robust Controller Design and System Norms for Large-scale Systems
Jun 1, 2023 18:31 — 18:31 TU Dresden
PDF Slides
Low-dimensional linear parameter varying system approximations for nonlinear controller design
May 25, 2023 11:00 — 11:45 Blacksburg, Virginia
PDF Slides
Numerical Methods in Control and Optimization of Dynamical Systems
Jan 16, 2023 14:00 TU Berlin
Slides
Data-driven identification of encoding on quadratic-manifolds for high-fidelity dynamical models
Jun 7, 2022 16:30 — Jun 7, 2021 17:00
Slides Preprint
Linearization errors as smooth perturbations of coprime factors in linearized Navier-Stokes equations
Sep 30, 2021 15:00 — 15:30 Uni Passau (virtual)
Slides
See all events

Recent Publications

Browse and filter all publications or see my CV with a list of publications.
A low-rank solution method for Riccati equations with indefinite quadratic terms
Algebraic Riccati equations with indefinite quadratic terms play an important role in applications related to robust controller design. …
Cite DOI Preprint
Convolutional Autoencoders, Clustering, and POD for Low-dimensional Parametrization of Navier-Stokes Equations
Cite DOI
Convolutional Autoencoders, Clustering, and POD for Low-dimensional Parametrization of Navier-Stokes Equations
Exponential Lag Synchronization of Cohen-Grossberg Neural Networks with Discrete and Distributed Delays on Time Scales
Cite DOI
Low-complexity LPV approximations of incompressible Navier-Stokes equations for truncated state-dependent Riccati feedback
Nonlinear feedback design via state-dependent Riccati equations is well established but unfeasible for large-scale systems because of …
Cite Preprint Code
Low-complexity LPV approximations of incompressible Navier-Stokes equations for truncated state-dependent Riccati feedback
Space and Chaos-Expansion Galerkin POD Low-order Discretization of PDEs for Uncertainty Quantification
The quantification of multivariate uncertainties in partial differential equations can easily exceed any computing capacity unless …
Cite arXiv
Space and Chaos-Expansion Galerkin POD Low-order Discretization of PDEs for Uncertainty Quantification
See all publications

Contact