6 citations · 19 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
GeoLoRA: Geometric integration for parameter efficient fine-tuning
Steffen Schotthöfer, Emanuele Zangrando, Gianluca Ceruti +2
Low-Rank Adaptation (LoRA) has become a widely used method for parameter-efficient fine-tuning of large-scale, pre-trained neural networks. However, LoRA and its extensions face se…
Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees
Steffen Schotthöfer, M. Paul Laiu
In this work, we propose a federated dynamical low-rank training (FeDLRT) scheme to reduce client compute and communication costs - two significant performance bottlenecks in horiz…
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…