341 citations
- Volkswagen Group (United States)US10 papers
- Technical University of MunichDE7 papers
- Eötvös Loránd UniversityHU5 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE5 papers
- RWTH Aachen UniversityDE5 papers
- Michigan State UniversityUS4 papers
- Technische Universität BraunschweigDE4 papers
- Ludwig-Maximilians-Universität MünchenDE3 papers
- Technische Universität DarmstadtDE3 papers
- Xanadu Quantum Technologies (Canada)CA3 papers
- BMW (Germany)DE2 papers
- École Polytechnique Fédérale de LausanneCH2 papers
7 papers · 1 filter
Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering
Francesco Romor, Marco Tezzele, Markus Mrosek +2
Multi-fidelity models are of great importance due to their capability of fusing information coming from different numerical simulations, surrogates, and sensors. We focus on the ap…
Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes
Sam Jacob Jacob, Markus Mrosek, Carsten Othmer +1
The aerodynamic optimization process of cars requires multiple iterations between aerodynamicists and stylists. Response Surface Modeling and Reduced-Order Modeling are commonly us…
Probabilistic Reconstruction of Type Ia Supernova SN 2002bo
John T. O'Brien, Wolfgang E. Kerzendorf, Andrew Fullard +8
Manual fits to spectral times series of Type Ia supernovae have provided a method of reconstructing the explosion from a parametric model but due to lack of information about model…
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben, Stephanie Abrecht, Maram Akila +38
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These s…
The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing
Andreas Bär, Jonas Löhdefink, Nikhil Kapoor +4
Enabling autonomous driving (AD) can be considered one of the biggest challenges in today's technology. AD is a complex task accomplished by several functionalities, with environme…
Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation
Felix Frank, Alexandros Paraschos, Patrick van der Smagt +1
Placing robots outside controlled conditions requires versatile movement representations that allow robots to learn new tasks and adapt them to environmental changes. The introduct…