45 citations · 95 across the 13 of their papers we have counts for
5 papers · 1 filter
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…