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On using machine learning algorithms for motorcycle collision detection
Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far …
Philipp Rodegast, Steffen Maier, Jonas Kneifl, Jörg Fehr
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Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction
Over the last decades, computer modeling has evolved from a supporting tool for engineering prototype design to an ubiquitous …
Jonas Kneifl, David Rosin, Okan Avci, Oliver Röhrle, Jörg Fehr
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Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators
We discuss nonlinear model predictive control (MPC) for multi-body dynamics via physics-informed machine learning methods. In more …
Jonas Nicodemus, Jonas Kneifl, Jörg Fehr, Benjamin Unger
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Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme
Recent research in non-intrusive data-driven model order reduction (MOR) enabled accurate and efficient approximation of parameterized …
J. Kneifl, J. Hay, J. Fehr
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Machine Learning Algorithms for Learning Nonlinear Terms of Reduced Mechanical Models in Explicit Structural Dynamics
Modeling and simulations are a pillar in the development of complex technical systems. However, for time-critical applications a …
Jonas Kneifl, Jörg Fehr
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A nonintrusive nonlinear model reduction method for structural dynamical problems based on machine learning
Abstract Model order reduction (MOR) has become one of the most widely used tools to create efficient surrogate models for …
Jonas Kneifl, Dennis Grunert, Joerg Fehr
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