Prof. Dr.-Ing. Lars Mikelsons

Lehrstuhlinhaber
Lehrstuhl für Mechatronik
Telefon: +49 821 598 69270
E-Mail:
Raum: 3053 (W)
Adresse: Am Technologiezentrum 8, 86159 Augsburg

Lebenslauf

Seit 2018      Inhaber des Lehrstuhls für Ingenieurinformatik mit Schwerpunkt Mechatronik an der Universität Augsburg

2016-2018    Project Manager bei Bosch
2011-2016    Development Engineer / Team Leader bei Bosch Rexroth

Forschungsschwerpunkte

  • Entwicklung zuverlässiger Methoden in der modellbasierten Entwicklung
  • Validierung von Systemkonzepten auf Basis virtueller Prototypen
  • Einbindung modellbasierter Methoden in moderne Entwicklungsprozesse

Lehrveranstaltungen

(Angewandte Filter: Semester: aktuelles | Dozenten: Lars Mikelsons | Vorlesungsarten: alle)
Name Semester Typ
Praktikum zu Objektorientierte Modellbildung und Simulation: Theorie und Praxis Wintersemester 2023/24 Praktikum
Oberseminar zur Mechatronik Wintersemester 2023/24 Oberseminar
Optimierung mechatronischer Systeme Wintersemester 2023/24 Vorlesung
Seminar zu Grundlagen der Mechatronik Wintersemester 2023/24 Seminar
Praktikum Mechatronik Wintersemester 2023/24 Praktikum
Objektorientierte Modellbildung und Simulation: Theorie und Praxis Wintersemester 2023/24 Vorlesung
Praktikum Konstruktionslehre Wintersemester 2023/24 Praktikum
Praktikum Simulation cyber-physischer Systeme Wintersemester 2023/24 Praktikum
Seminar zu aktuellen Forschungsthemen der Mechatronik Wintersemester 2023/24 Seminar
Übung zu Optimierung mechatronischer Systeme Wintersemester 2023/24 Übung
Praktikum Autonomes Fahren Wintersemester 2023/24 Praktikum
Übung zu Objektorientierte Modellbildung und Simulation: Theorie und Praxis Wintersemester 2023/24 Übung

Publikationen

2023 | 2022 | 2021 | 2020 | 2017 | 2015 | 2014 | 2012 | 2011 | 2009 | 2008 | 2007

2023

Damian Boborzi, Christoph-Nikolas Straehle, Jens S. Buchner and Lars Mikelsons. 2023. Imitation learning by state-only distribution matching. DOI: 10.1007/s10489-023-05062-w
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Tobias Thummerer, Artem Kolesnikov, Julia Gundermann, Denis Ritz and Lars Mikelsons. 2023. Paving the way for hybrid twins using neural functional mock-up units. DOI: 10.3384/ecp204141
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Yi Zhang and Lars Mikelsons. 2023. Sensitivity-guided iterative parameter identification and data generation with BayesFlow and PELS-VAE for model calibration. DOI: 10.1186/s40323-023-00246-y
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2022

Tobias Thummerer, Johannes Stoljar and Lars Mikelsons. 2022. NeuralFMU: presenting a workflow for integrating hybrid neuralODEs into real-world applications. DOI: 10.3390/electronics11193202
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Monika E. Heringhaus, Yi Zhang, André Zimmermann and Lars Mikelsons. 2022. Towards reliable parameter extraction in MEMS final module testing using Bayesian inference. DOI: 10.3390/s22145408
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2021

Tobias Thummerer, Johannes Tintenherr and Lars Mikelsons. 2021. Hybrid modeling of the human cardiovascular system using NeuralFMUs. DOI: 10.1088/1742-6596/2090/1/012155
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Frederic Bruder and Lars Mikelsons. 2021. Modia and Julia for grey box modeling. DOI: 10.3384/ecp2118187
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Tobias Thummerer, Lars Mikelsons and Josef Kircher. 2021. NeuralFMU: towards structural integration of FMUs into neural networks. DOI: 10.3384/ecp21181297
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2020

Tobias Thummerer and Lars Mikelsons. 2020. A tetrahedron-shaped inverted pendulum with torque-actuation [Extended abstract]. DOI: 10.17185/duepublico/71191
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Tobias Thummerer and Lars Mikelsons. 2020. Modeling and control of a tetrahedron-shaped inverted pendulum with torque-actuation. DOI: 10.1016/j.ifacol.2020.12.2667
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2017

Lars Mikelsons and Roland Samlaus. 2017. Towards Virtual Validation of ECU Software using FMI. DOI: 10.3384/ecp17132307
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2015

Andreas Hofmann, Nils Menager, Issam Belhaj and Lars Mikelsons. 2015. Integrated Engineering based on Modelica. DOI: 10.3384/ecp15118893
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Andreas Hofmann, Nils Menager, Stephan Schweig and Lars Mikelsons. 2015. Model-Based Engineering mit Industriesteuerungen.
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Nils Menager, Lars Mikelsons and Niklas Worschech. 2015. Modellbasierte Entwicklung mit Rexroth-Steuergeräten unter Nutzung von offenen Standards. DOI: 10.17877/DE290R-7388
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Nils Menager, Rüdiger Kampfmann, Niklas Worschech and Lars Mikelsons. 2015. Suitability of Different Real-Time Solvers for a Model-Based Engineering Toolchain using Industrial Rexroth Controllers. DOI: 10.3384/ecp15118883
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Andreas Hofmann, Stephan Schweig and Lars Mikelsons. 2015. Virtuelle Inbetriebnahme mechatronischer Systeme unter Einbeziehung realer Industriesteuerungen von Bosch Rexroth. DOI: 10.17877/DE290R-7388
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2014

Nils Menager, Niklas Worschech and Lars Mikelsons. 2014. A toolchain for Rapid Control Prototyping using Rexroth controllers and open source software. DOI: 10.3384/ecp14096371
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Andreas Hofmann, Lars Mikelsons, Ines Gubsch and Christian Schubert. 2014. Simulating Collisions within the Modelica MultiBody library. DOI: 10.3384/ecp14096949
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Lars Mikelsons and Zhou Su. 2014. Simulation for verification and validation of functional safety. DOI: 10.3384/ecp14096455
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2012

Tobias Bruckmann, Lars Mikelsons and Manfred Hiller. 2012. A Design-To-Task Approach for Wire Robots. DOI: 10.1007/978-94-007-2978-0_6
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Niklas Worschech and Lars Mikelsons. 2012. A Toolchain for Real-Time Simulation using the OpenModelica Compiler. DOI: 10.3384/ecp12076839
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Lars Mikelsons and Thorsten Brandt. 2012. Towards a Generic Vehicle Model. DOI: 10.1115/1.4005438
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Hongchao Ji, Lars Mikelsons, Karl Kempf and Dieter Schramm. 2012. Using Static Parametric Design to Support Systems Engineering of Industrial Automation Systems. DOI: 10.3384/ecp12076971
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2011

Lars Mikelsons and Thorsten Brandt. 2011. Generation of continuously adjustable vehicle models using symbolic reduction methods. DOI: 10.1007/s11044-011-9247-x
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Lars Mikelsons, Nils Menager and Dieter Schramm. 2011. Partitioned Model vs Parallelized Solver. DOI: 10.1115/detc2011-47645
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Lars Mikelsons, Thorsten Brandt and Dieter Schramm. 2011. Real-Time Vehicle Dynamics Using Equation-Based Reduction Techniques. DOI: 10.1007/978-94-007-1643-8_11
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2009

Tobias Bruckmann, Lars Mikelsons, Thorsten Brandt, Dieter Schramm, Andreas Pott and Moustafa Abdel-Maksoud. 2009. A Novel Tensed Mechanism for Simulation of Maneuvers in Wind Tunnels. DOI: 10.1115/detc2009-86718
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Andreas Pott, Tobias Bruckmann and Lars Mikelsons. 2009. Closed-form force distribution for parallel wire robots. DOI: 10.1007/978-3-642-01947-0_4
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Tobias Bruckmann, Lars Mikelsons, Thorsten Brandt, Manfred Hiller and Dieter Schramm. 2009. Design Approaches for Wire Robots. DOI: 10.1115/detc2009-86720
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Lars Mikelsons, Hongchao Ji, Thorsten Brandt and Oliver Lenord. 2009. Symbolic Model Reduction Applied to Realtime Simulation of a Construction Machine. DOI: 10.3384/ecp09430136
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Lars Mikelsons and Thorsten Brandt. 2009. Symbolic Model Reduction for Interval-Valued Scenarios. DOI: 10.1115/detc2009-86954
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2008

Lars Mikelsons, Tobias Bruckmann, Manfred Hiller and Dieter Schramm. 2008. A real-time capable force calculation algorithm for redundant tendon-based parallel manipulators. DOI: 10.1109/robot.2008.4543805
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Tobias Bruckmann, Lars Mikelsons, Dieter Schramm and Manfred Hiller. 2008. Analysis and synthesis of parallel tendon-based manipulators.
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Tobias Bruckmann, Lars Mikelsons, Manfred Hiller and Dieter Schramm. 2008. Continuous Workspace Analysis, Synthesis and Optimization of Wire Robots. DOI: 10.1115/detc2008-49532
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Tobias Bruckmann, Lars Mikelsons, Thorsten Brandt, Manfred Hiller and Dieter Schramm. 2008. Wire Robots Part I: Kinematics, Analysis & Design. DOI: 10.5772/5365
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Tobias Bruckmann, Lars Mikelsons, Thorsten Brandt, Manfred Hiller and Dieter Schramm. 2008. Wire Robots Part II: Dynamics, Control & Application. DOI: 10.5772/5366
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2007

Tobias Bruckmann, Lars Mikelsons, Manfred Hiller and Dieter Schramm. 2007. A new force calculation algorithm for tendon-based parallel manipulators. DOI: 10.1109/aim.2007.4412432
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Tobias Bruckmann, Lars Mikelsons, Dieter Schramm and Manfred Hiller. 2007. Continuous workspace analysis for parallel cable-driven Stewart-Gough platforms. DOI: 10.1002/pamm.200700774
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Software

FMI.jl

Softwarebibliothek zur Integration des Functional Mock-up Interface (FMI) in die Julia Programmiersprache.

Github: FMI.jl

FMIFlux.jl

Machine Learning mit hybriden Modellen aus Functional Mock-up Units (FMUs) in der Julia Programmiersprache.

Github: FMIFlux.jl

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