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Bild  Eva Dierkes

Eva Dierkes

Research Assistant WG Optimization and Optimal Control, Research Training Group π3

Room: MZH 2080
Email: eva.dierkes@uni-bremen.de
Phone: (0421) 218-64357
ORCID iD:  0000-0002-9243-1494

Part of the Women's Representative Collective of Department 3

CV

Theses

Master Thesis(12/2018)

Identification and mapping of surface arameters based on optimal control and machine learning
Supervisors: 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

Projects

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

Publications (Selection)complete list

  1. 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.

    DOI: 10.1002/pamm.202000286

  2. 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.
  3. 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.
  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.