Publications

Johannes Rettberg, Jonas Kneifl, Julius Herb, Patrick Buchfink, Jörg Fehr, Bernard Haasdonk (2024). Data-driven identification of latent port-Hamiltonian systems.

Cite DOI Code URL

Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz (2024). VENI, VINDy, VICI: a variational reduced-order modeling framework with uncertainty quantification. arXiv.

Cite DOI Code arXiv

Philipp Rodegast, Steffen Maier, Jonas Kneifl, Jörg Fehr (2024). On using machine learning algorithms for motorcycle collision detection. Springer Science and Business Media LLC.

Jonas Kneifl, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz (2024). Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks. Computational Mechanics.

Jonas Kneifl, David Rosin, Okan Avci, Oliver Röhrle, Jörg Fehr (2023). Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction. Archive of Applied Mechanics.

J. Kneifl, J. Hay, J. Fehr (2022). Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme. IFAC-PapersOnLine.

Jonas Nicodemus, Jonas Kneifl, Jörg Fehr, Benjamin Unger (2022). Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators. IFAC-PapersOnLine.

Jonas Kneifl, Jörg Fehr (2021). Machine Learning Algorithms for Learning Nonlinear Terms of Reduced Mechanical Models in Explicit Structural Dynamics. PAMM.

Jonas Kneifl, Dennis Grunert, Joerg Fehr (2021). A nonintrusive nonlinear model reduction method for structural dynamical problems based on machine learning. International Journal for Numerical Methods in Engineering.