Jan Heiland Jan Heiland

Lecturer

TU Ilmenau

MPI Magdeburg

About Me

Researcher and lecturer in the working group Optimization-based control at the TU Ilmenau.

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Interests

• Differential-algebraic Equations
• Systems and Control Theory
• Data-driven Modelling
• Flow Control

Education

• PhD Applied Mathematics

  TU Berlin

Teaching

Current Classes (Winter Term 2024)

Topic	Location
Lecture + Exercise: Numerical Solutions of Partial Differential Equations	🎤 Do 11-13 im F-HS und Fr 9-11 (nur gerade Woche) im C110	Moodle
Lecture + Exercise: Wissenschaftliches Rechnen 1	🎤 Di 11-13 und Do 13-15 im C115	Moodle

Previous Courses (Summer Term 2024)

Topic
Numerik 2 (des maschinellen Lernens)	Moodle, Skript
Wissenschaftliches Rechnen	Moodle
Numerische Mathematik	Moodle

Selected Projects

Grants, Bachelor/Master/PhD Projects, and others.

Grants

LPV Approximations for Nonlinear Controller Design

Project Description The computer-aided control system design for nonlinear large-dimensional systems is a tremendous task as it has to cope both with model complexities due to nonlinear dynamics and with computational challenges that are caused by the system’s size.

Jul 20, 2023

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).

Apr 20, 2023

Grants

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.

Apr 20, 2023

BA-MA

MRI Fingerprinting with Philips

on *Improving Fingerprinting in MRI scans*

Apr 15, 2021

BA-MA

Open Master Thesis

on *DMD models for flow problems based on time series using adaptive grids*

Jan 1, 2020

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Featured Publications

Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows

Jan 1, 2024

Low-Complexity Linear Parameter-Varying Approximations of Incompressible Navier-Stokes Equations for Truncated State-Dependent Riccati Feedback

Jan 1, 2023

Recent Publications

Jan Heiland, Yongho Kim, Steffen W. R. Werner (2024). Deep Polytopic Autoencoders for Low-dimensional Linear Parameter-varying Approximations and Nonlinear Feedback Design.

Cite DOI arXiv

Jan Heiland, Yongho Kim (2024). Polytopic Autoencoders with Smooth Clustering for Reduced-order Modelling of Flows.

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Peter Benner, Jan Heiland, Steffen W. R. Werner (2023). A low-rank solution method for Riccati equations with indefinite quadratic terms. Numerical Algorithms.

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P. Benner, P. Goyal, J. Heiland, I. Pontes (2023). A quadratic decoder approach to nonintrusive reduced-order modeling of nonlinear dynamical systems. Proceedings in Applied Mathematics & Mechanics.

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Y. Kim, J. Heiland (2023). Convolutional Autoencoders, Clustering, and POD for Low-dimensional Parametrization of Navier-Stokes Equations.

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Recent & Upcoming Talks

Implicit and Explicit Matching of Non-Proper Transfer Functions in the Loewner Framework

Jun 28, 2024

Two-step MOR for H-infinity-robust nonlinear controller design

Mar 21, 2024

Advanced Topics in Systems and Control

A serious of talks and short lectures hosted by the Jiangsu University of Science and Technology.

Nov 23, 2023

Open Access

The DEAL is here - What now?

An update to my 2020 talk and takes on the DEAL.

Oct 23, 2023

Computational Approaches to H-Infinity-robust Controller Design and System Norms for Large-scale Systems

The computation of controller gains for the (linearization based) $H_\infty$ robust control of linear (and nonlinear) dynamical systems amounts to the solution of indefinite Riccati equations. For large-scale systems, a direct approach is infeasible because of both computational efforts and memory requirements.

Jun 1, 2023

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Blog Posts

Ausbreitung von Glitzer und anderen ansteckenden Eigenschaften

Für SchülerUnis bietet sich die Modellierung von Epidemien an, weil die Mechanismen und die Mathematik im Modell nachvollziehbar sind. Bisher hatte mich an meinen Versuchen damit aber gestört, dass das Thema etwas trübe ist (niemand hat Lust auf Corona oder einen Zombie) Es doch abstrakte Mathe bleibt (wenn beispielsweise im deterministischen “SIR”-Modell die Ansteckungsraten einfach angenommen werden) und letztlich die Verbindung zwischen Modell und Person nicht hergestellt wird.

Jul 7, 2024

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

Productivity

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

Trends in Covid-19 Spread

How the daily changes in the cases may indicate whether the virus is on the decline.

Mar 22, 2020

FEniCS Burgers POD DMD

A short python implementation of POD and DMD for a 2D Burgers equation using FEniCS and Scipy.

Mar 7, 2020

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