This image shows Jonas Kneifl

Jonas Kneifl

M.Sc.

Institut für Technische und Numerische Mechanik (ITM)

Contact

+49 711 685 66414
+49 711 685 66400

Pfaffenwaldring 9
70569 Stuttgart
Deutschland
Room: 3.101

Subject

  1. 2023

    1. J. Kneifl, D. Rosin, O. Avci, O. Röhrle, and J. Fehr, “Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction,” Archive of Applied Mechanics, Jun. 2023, doi: 10.1007/s00419-023-02458-5.
    2. J. Kneifl and J. Fehr, “Crash Simulations of a Racing Kart’s Structural Frame Colliding against a Rigid Wall.” DaRUS, 2023. doi: 10.18419/DARUS-3789.
  2. 2022

    1. J. Kneifl, J. Hay, and J. Fehr, “Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme,” IFAC-PapersOnLine, vol. 55, no. 20, Art. no. 20, 2022, doi: 10.1016/j.ifacol.2022.09.109.
    2. J. Nicodemus, J. Kneifl, J. Fehr, and B. Unger, “Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators,” IFAC-PapersOnLine, vol. 55, no. 20, Art. no. 20, 2022, doi: 10.1016/j.ifacol.2022.09.117.
  3. 2021

    1. J. Kneifl, D. Grunert, and J. Fehr, “A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning,” International Journal for Numerical Methods in Engineering, Apr. 2021, doi: 10.1002/nme.6712.
    2. J. Kneifl and J. Fehr, “Machine Learning Algorithms for Learning Nonlinear Terms of Reduced Mechanical Models in Explicit Structural Dynamics,” PAMM, vol. 20, no. S1, Art. no. S1, Mar. 2021, doi: 10.1002/pamm.202000353.

14. September 2023: Seminar Visit @ DICA, Politecnico Milano, Milan: "Data-driven Surrogate Modeling of Structural Dynamical Systems"

05. July 2023: Mechanics Seminar, University of Brasília Mathematics Department, Brasília: „Data-driven Surrogate Modeling of Structural Dynamical Systems"

09. May 2023: Campus Feminarum, Stuttgart: „Parsimonious Human Body Models Using Data-driven Model Reduction”

29. February 2023: SIAM Conference on Computational Science and Engineering (CSE23), Amsterdam: „Multi-hierarchic Data-driven Reduced Order Models based on Mesh Simplification and Graph Convolutional Autoencoders”

27. January 2023: SimTech Seminar on Model Reduction and Data Techniques for
Surrogate Modelling, Stuttgart: „Surrogate Models from Simulation Data in Structural Dynamics”

07. December 2022: MOR-Day, Stuttgart: „Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme”

27. July 2022: 10th Vienna International Conference on Mathematical Modelling, Vienna, Austria: „Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme”

12. January 2022: Virtual talk at the ML Session on “Data Spotlight: Data-driven methods for engineering applications": „Data-driven Model Reduction
for Structural Dynamical Problems"

20. October 2020: Virtual talk at the 6th GAMM AG DATA Workshop; „Machine Learning Algorithms for Learning Nonlinear Terms of Reduced Mechanical Models in Explicit Structural Dynamics"

 

Kneifl, Jonas; Rosin, David; Avci, Okan; Röhrle, Oliver; Fehr, Jörg, 2023, "Continuum-mechanical Forward Simulation Results of a Human Upper-limb Model Under Varying Muscle Activations", https://doi.org/10.18419/darus-3302, DaRUS, V1

Kneifl, Jonas; Hay, Julian; Fehr, Jörg, 2022, "Human Occupant Motion in Pre-Crash Scenario", https://doi.org/10.18419/darus-2471, DaRUS, V1

Kneifl, Jonas; Fehr, Jörg, 2020, "Deformation of a Structural Frame of a Racing Kart Colliding against a Rigid Wall", https://doi.org/10.18419/darus-1150, DaRUS, V1

22. March 2023: Beiratssiztung InnovationsCampus Mobilität: „Transportable echtzeitfähige digitale Zwillinge (TEDZ)”

05. July 2022: SimTech Statusseminar, Bad Boll: „Reusage and Reanalysis of Simulation Data"

 

"Low-Dimensional Discovery of Port-Hamiltonian Systems by Combining Model Order Reduction and Machine Learning", Research module SimTech.
Institute of Engineering and Computational Mechanics, University of Stuttgart, 2023.
In co-supervision with Johannes Rettberg, M.Sc.

"Realisierung einer experimentellen Insassen-Sitz-Interaktionsstudie bei Variation der Sitzposition einschliesslich eines Versuchsaufbaus samt Datenerfassung zur Generierung von Ersatzmodellen", Master’s thesis MSC-340. Institute of Engineering and Computational Mechanics, University of Stuttgart, 2023. Supervision together with Niklas Fahse, M.Sc.

"Investigation of the suitability of surrogate models for predicting human-seat interaction with varying seat position using human body models", Student thesis. Institute of Engineering and Computational Mechanics, University of Stuttgart, 2022. Joined supervision with Fabian Kempter, M.Sc.

"Collision detection of a motorcycle in accident scenarios using machine learning algorithms", Master’s thesis. Institute of Engineering and Computational Mechanics, University of Stuttgart, 2022. Joined supervision with Steffen Maier, M.Sc.

"Discovering Friction Models from Experimental Data using Physics-informed Neural Networks", Forschungsmodul. Institut für Parallele und Verteilte Systeme / Institute of Engineering and Computational Mechanics, University of Stuttgart, 2022.

"Application of Linear Model Order Reduction Methods to Accelerate Nonlinear Crash Simulations", Student thesis. Institute of Engineering and Computational Mechanics, University of Stuttgart, 2022.

"Matrixapproximation mittels CUR Zerlegung", Sonstige Arbeit SA-34. Institut für Technische und Numerische Mechanik, Universität Stuttgart, 2021.

"Application of Physics Informed Neural Networks for the Approximation of Differential Equations in the Field of Rigid Body Dynamics", Master’s thesis MSC-316. Institut für Technische und Numerische Mechanik, Universität Stuttgart, 2021.

"Untersuchung der Eignung von Ersatzmodellen zur Analyse des Haltungseinflusses bei Heckaufprallevents anhand eines Hals-Nackenmodells", Student thesis STUD-510. Institut für Technische und Numerische Mechanik, Universität Stuttgart, 2021. Betreuung gemeinsam mit Fabian Kempter,M.Sc.

To the top of the page