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Steffen Schotthöfer
Steffen Schotthöfer
PhD Candidate in Mathematics, KIT Karlsruhe
Verified email at kit.edu
Title
Cited by
Cited by
Year
A Numerical Comparison of Consensus‐Based Global Optimization to other Particle‐based Global Optimization Schemes
C Totzeck, R Pinnau, S Blauth, S Schotthöfer
PAMM 18 (1), e201800291, 2018
132018
A structure-preserving surrogate model for the closure of the moment system of the Boltzmann equation using convex deep neural networks
S Schotthöfer, T Xiao, M Frank, C Hauck
AIAA AVIATION 2021 FORUM, 2895, 2021
52021
Neural network-based, structure-preserving entropy closures for the Boltzmann moment system
S Schotthöfer, T Xiao, M Frank, CD Hauck
arXiv preprint arXiv:2201.10364, 2022
32022
Windowing regularization techniques for unsteady aerodynamic shape optimization
S Schotthöfer, BY Zhou, TA Albring, NR Gauger
AIAA AVIATION 2020 FORUM, 3130, 2020
32020
Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation
S Schotthöfer, T Xiao, M Frank, CD Hauck
Proceedings of the International Conference on Machine Learning, PMLR …, 2022
12022
Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations
S Schotthöfer, E Zangrando, J Kusch, G Ceruti, F Tudisco
arXiv preprint arXiv:2205.13571, 2022
12022
KiT-RT: An extendable framework for radiative transfer and therapy
J Kusch, S Schotthöfer, P Stammer, J Wolters, T Xiao
arXiv preprint arXiv:2205.08417, 2022
2022
Predicting continuum breakdown with deep neural networks
T Xiao, S Schotthöfer, M Frank
arXiv preprint arXiv:2203.02933, 2022
2022
Regularization for Adjoint-Based Unsteady Aerodynamic Optimization Using Windowing Techniques
S Schotthöfer, BY Zhou, T Albring, NR Gauger
AIAA Journal 59 (7), 2517-2531, 2021
2021
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