3 citations · 5 across the 6 of their papers we have counts for
7 papers · 1 filter
An Augmented Backward-Corrected Projector Splitting Integrator for Dynamical Low-Rank Training
Jonas Kusch, Steffen Schotthöfer, Alexandra Walter
Layer factorization has emerged as a widely used technique for training memory-efficient neural networks. However, layer factorization methods face several challenges, particularly…
Windowing Regularization Techniques for Unsteady Aerodynamic Shape Optimization
Steffen Schotthöfer, Beckett Y. Zhou, Tim Albring +1
Unsteady Aerodynamic Shape Optimization presents new challenges in terms of sensitivity analysis of time-dependent objective functions. In this work, we consider periodic unsteady…
Structure-preserving neural networks for the regularized entropy-based closure of the Boltzmann moment system
Steffen Schotthöfer, M. Paul Laiu, Martin Frank +1
The main challenge of large-scale numerical simulation of radiation transport is the high memory and computation time requirements of discretization methods for kinetic equations.…
Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations
Jae Yong Lee, Steffen Schotthöfer, Tianbai Xiao +2
This work explores the application of deep operator learning principles to a problem in statistical physics. Specifically, we consider the linear kinetic equation, consisting of a…
Construction of high-order conservative basis-update and Galerkin dynamical low-rank integrators
Lukas Einkemmer, Jonas Kusch, Steffen Schotthöfer
Numerical simulations of kinetic problems can become prohibitively expensive due to their large memory requirements and computational costs. A method that has proven to successfull…
Neural network-based, structure-preserving entropy closures for the Boltzmann moment system
Steffen Schotthöfer, Tianbai Xiao, Martin Frank +1
This work presents neural network based minimal entropy closures for the moment system of the Boltzmann equation, that preserve the inherent structure of the system of partial diff…