45 citations · 95 across the 13 of their papers we have counts for
13 papers
Towards Using Active Learning Methods for Human-Seat Interactions To Generate Realistic Occupant Motion
Niklas Fahse, Monika Harant, Marius Obentheuer +2
In the context of developing new vehicle concepts, especially autonomous vehicles with novel seating arrangements and occupant activities, predicting occupant motion can be a tool…
Data-driven identification of latent port-Hamiltonian systems
Johannes Rettberg, Jonas Kneifl, Julius Herb +3
Conventional physics-based modeling techniques involve high effort, e.g., time and expert knowledge, while data-driven methods often lack interpretability, structure, and sometimes…
VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification
Paolo Conti, Jonas Kneifl, Andrea Manzoni +4
The simulation of many complex phenomena in engineering and science requires solving expensive, high-dimensional systems of partial differential equations (PDEs). To circumvent thi…
On using Machine Learning Algorithms for Motorcycle Collision Detection
Philipp Rodegast, Steffen Maier, Jonas Kneifl +1
Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far exceeds passenger car accidents, efforts have…
Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks
Jonas Kneifl, Jörg Fehr, Steven L. Brunton +1
Crash simulations play an essential role in improving vehicle safety, design optimization, and injury risk estimation. Unfortunately, numerical solutions of such problems using sta…
Low-dimensional Data-based Surrogate Model of a Continuum-mechanical Musculoskeletal System Based on Non-intrusive Model Order Reduction
Jonas Kneifl, David Rosin, Oliver Röhrle +1
In recent decades, the main focus of computer modeling has been on supporting the design and development of engineering prototyes, but it is now ubiquitous in non-traditional areas…
Dynamic Human Body Models in Vehicle Safety: An Overview
Niklas Fahse, Matthew Millard, Fabian Kempter +3
Significant trends in the vehicle industry are autonomous driving, micromobility, electrification and the increased use of shared mobility solutions. These new vehicle automation a…
Identification of Friction Models for MPC-based Control of a PowerCube Serial Robot
Jörg Fehr, Arnim Kargl, Hannes Eschmann
For model-based control, an accurate and in its complexity suitable representation of the real system is a decisive prerequisite for high and robust control quality. In a structure…
Port-Hamiltonian Fluid-Structure Interaction Modeling and Structure-Preserving Model Order Reduction of a Classical Guitar
Johannes Rettberg, Dominik Wittwar, Patrick Buchfink +4
A fluid-structure interaction model in a port-Hamiltonian representation is derived for a classical guitar. We combine the laws of continuum mechanics for solids and fluids within…
Real-time Human Response Prediction Using a Non-intrusive Data-driven Model Reduction Scheme
Jonas Kneifl, Julian Hay, Jörg Fehr
Recent research in non-intrusive data-driven model order reduction (MOR) enabled accurate and efficient approximation of parameterized ordinary differential equations (ODEs). Howev…
Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators
Jonas Nicodemus, Jonas Kneifl, Jörg Fehr +1
We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising…
Sustainable Research Software Hand-Over
Jörg Fehr, Christian Himpe, Stephan Rave +1
Scientific software projects evolve rapidly in their initial development phase, yet at the end of a funding period, the completion of a research project, thesis, or publication, fu…
Best Practices for Replicability, Reproducibility and Reusability of Computer-Based Experiments Exemplified by Model Reduction Software
Jörg Fehr, Jan Heiland, Christian Himpe +1
Over the recent years the importance of numerical experiments has gradually been more recognized. Nonetheless, sufficient documentation of how computational results have been obtai…