Eva Dierkes

CV

Theses

Master Thesis(12/2018)

Identification and mapping of surface arameters based on optimal control and machine learning
Supervisors: Prof. Dr. Kathrin Flaßkamp, Prof. Dr. Christof Büskens
Universität Bremen

Bachelor Thesis (08/2016)

Acoustic positioning in the area
Supervisors: Prof. Dr. Christof Büskens, Dr. Matthias Knauer
Universität Bremen

Arbeit & Forschung

PhD Student RTG π³ (seit 09/2019)

R2-7: Parameter and Structure Identification for Complex Dynamical Systems
Center for Industrial Mathematics
University of Bremen

Research Assistant (since 01/2019)

Optimization and Optimal Control
Center for Industrial Mathematics
University of Bremen

Internship (08/2017 - 10/2017)

Modeling of industrial production processes with artificial neuronal networks
IAV GmbH
Giffhorn, Deutschland

Student Assistant (06/2015 - 12/2018)

CAUSE-Cognitive Autonomous Subsurface Exploration
Optimization and Optimal Control
University of Bremen

Research Areas

• Parameter identification for dynamical systems
• Optimization
• Optimal control

Projects

1. SmartFarm2 (01.02.2021 - 31.08.2024)
2. SmartDrive (01.11.2018 - 30.04.2020)
3. CAUSE-Cognitive Autonomous Subsurface Exploration (01.04.2015 - 30.09.2018)

Publications (Selection)complete list

1. E. Dierkes, C. Offen, S. Ober-Blöbaum, K. Flaßkamp.
   Hamiltonian neural networks with automatic symmetry detection.
   Chaos: an Interdisciplinary Journal of Nonlinear Science, 33(6), 2023.

   DOI: 10.1063/5.0142969

2. E. Dierkes, F. Jung, C. Büskens.
   Data-based models of drive technology for automation in automotive production.
   GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
   Proceedings in Applied Mathematics & Mechanics, 20(1), WILEY-VCH, 2021.

   DOI: 10.1002/pamm.202000286

3. E. Dierkes, C. Meerpohl, K. Flaßkamp, C. Büskens.
   Estimation and Mapping of System-Surface Interaction by Combining Nonlinear Optimization and Machine Learning.
   Third IFAC Conference on Modelling, Identification and Control of Nonlinear Systems, 15.09-17.09.2021.
   

   DOI: 10.1016/j.ifacol.2021.10.342

4. E. Dierkes, K. Flaßkamp.
   Learning Mechanical Systems by Hamiltonian Neural Networks.
   GAMM 91st Annual Meeting of the international Association of Applied Mathematics and Mechanics, online, 15.03.2021 - 19.03.2021.
   Proceedings in Applied Mathematics & Mechanics, 21(1), WILEY-VCH, 2021.

   DOI: 10.1002/pamm.202100116

5. E. Dierkes, K. Flaßkamp.
   Learning Hamiltonian Systems considering System Symmetries in Neural Networks.
   The 7th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control, 11.10-13.10.2021, Berlin, Germany.
   

   DOI: 10.1016/j.ifacol.2021.11.080