Dieses Bild zeigt Jonas Kneifl

Jonas Kneifl

Herr M.Sc.

Institut für Technische und Numerische Mechanik (ITM)

Kontakt

+49 711 685 66414
+49 711 685 66400

Pfaffenwaldring 9
70569 Stuttgart
Deutschland
Raum: 3.101

Fachgebiet

  1. 2023

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

    1. Kneifl, J., Hay, J., & Fehr, J. (2022). Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme. IFAC-PapersOnLine, 55(20), Article 20. https://doi.org/10.1016/j.ifacol.2022.09.109
    2. Nicodemus, J., Kneifl, J., Fehr, J., & Unger, B. (2022). Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators. IFAC-PapersOnLine, 55(20), Article 20. https://doi.org/10.1016/j.ifacol.2022.09.117
  3. 2021

    1. Kneifl, J., Grunert, D., & Fehr, J. (2021). A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning. International Journal for Numerical Methods in Engineering. https://doi.org/10.1002/nme.6712
    2. Kneifl, J., & Fehr, J. (2021). Machine Learning Algorithms for Learning Nonlinear Terms of Reduced Mechanical Models in Explicit Structural Dynamics. PAMM, 20(S1), Article S1. https://doi.org/10.1002/pamm.202000353

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

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

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

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

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

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

12. Januar 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. Oktober 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", Forschungsmodul SimTech.
Universität Stuttgart, Institut für Technische und Numerische Mechanik, 2023.
Betreuung gemeinsam mit Johannes Rettberg, M.Sc.

"Realisierung einer experimentellen Insassen-Sitz-Interaktionsstudie bei Variation der Sitzposition einschliesslich eines Versuchsaufbaus samt Datenerfassung zur Generierung von Ersatzmodellen", Masterarbeit 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", Studienarbeit. Institut für Technische und Numerische Mechanik, Universität Stuttgart, 2022. Betreuung gemeinsam mit Fabian Kempter, M.Sc.

"Collision detection of a motorcycle in accident scenarios using machine learning algorithms", Masterarbeit. Institut für Technische und Numerische Mechanik, Universität Stuttgart, 2022. Betreuung gemeinsam mit Steffen Maier, M.Sc.

"Discovering Friction Models from Experimental Data using Physics-informed Neural Networks", Forschungsmodul. Institut für Parallele und Verteilte Systeme / Institut für Technische und Numerische Mechanik, Universität Stuttgart, 2022.

"Application of Linear Model Order Reduction Methods to Accelerate Nonlinear Crash Simulations", Studienarbeit. Institut für Technische und Numerische Mechanik, Universität 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", Masterarbeit 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", Studienarbeit STUD-510. Institut für Technische und Numerische Mechanik, Universität Stuttgart, 2021. Betreuung gemeinsam mit Fabian Kempter,M.Sc.

Zum Seitenanfang