A structure-preserving surrogate model for the closure of the moment system of the Boltzmann equation using convex deep neural networks. - Steffen Schotthöfer; Tianbai Xiao; Martin Frank 004; Cory D. Hauck (CoRR, 2021)

Structure Preserving Neural Networks: A Case Study in the Entropy Closure of the Boltzmann Equation. - Steffen Schotthöfer; Tianbai Xiao; Martin Frank 004; Cory D. Hauck (ICML, 2022)

Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations. - Steffen Schotthöfer; Emanuele Zangrando; Jonas Kusch; Gianluca Ceruti; Francesco Tudisco (NeurIPS, 2022)

Neural network-based, structure-preserving entropy closures for the Boltzmann moment system. - Steffen Schotthöfer; Tianbai Xiao; Martin Frank 004; Cory D. Hauck (CoRR, 2022)

Rank-adaptive spectral pruning of convolutional layers during training. - Emanuele Zangrando; Steffen Schotthöfer; Gianluca Ceruti; Jonas Kusch; Francesco Tudisco (CoRR, 2023)

Conservation properties of the augmented basis update & Galerkin integrator for kinetic problems. - Lukas Einkemmer; Jonas Kusch; Steffen Schotthöfer (CoRR, 2023)

Predicting continuum breakdown with deep neural networks. - Tianbai Xiao; Steffen Schotthöfer; Martin Frank 004 (J. Comput. Phys., 2023)

KiT-RT: An Extendable Framework for Radiative Transfer and Therapy. - Jonas Kusch; Steffen Schotthöfer; Pia Stammer; Jannick Wolters; Tianbai Xiao (ACM Trans. Math. Softw., 2023)

Geometry-aware training of factorized layers in tensor Tucker format. - Emanuele Zangrando; Steffen Schotthöfer; Gianluca Ceruti; Jonas Kusch; Francesco Tudisco (NeurIPS, 2024)

Structure-Preserving Operator Learning: Modeling the Collision Operator of Kinetic Equations. - Jae Yong Lee; Steffen Schotthöfer; Tianbai Xiao; Sebastian Krumscheid; Martin Frank 004 (CoRR, 2024)

Structure-preserving neural networks for the regularized entropy-based closure of the Boltzmann moment system. - Steffen Schotthöfer; M. Paul Laiu; Martin Frank 004; Cory D. Hauck (CoRR, 2024)

Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees. - Steffen Schotthöfer; M. Paul Laiu (CoRR, 2024)

Windowing Regularization Techniques for Unsteady Aerodynamic Shape Optimization. - Steffen Schotthöfer; Beckett Y. Zhou; Tim Albring; Nicolas R. Gauger (CoRR, 2024)

GeoLoRA: Geometric integration for parameter efficient fine-tuning. - Steffen Schotthöfer; Emanuele Zangrando; Gianluca Ceruti; Francesco Tudisco; Jonas Kusch (ICLR, 2025)

Structure-preserving neural networks for the regularized entropy-based closure of a linear, kinetic, radiative transport equation. - Steffen Schotthöfer; M. Paul Laiu; Martin Frank 004; Cory D. Hauck (J. Comput. Phys., 2025)

An Augmented Backward-Corrected Projector Splitting Integrator for Dynamical Low-Rank Training. - Jonas Kusch; Steffen Schotthöfer; Alexandra Walter (CoRR, 2025)

Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks. - Steffen Schotthöfer; H. Lexie Yang; Stefan Schnake (CoRR, 2025)

A geometric framework for momentum-based optimizers for low-rank training. - Steffen Schotthöfer; Timon Klein; Jonas Kusch (CoRR, 2025)

Data-Driven Filtering of the Spherical Harmonics Method. - Benjamin Plumridge; Cory D. Hauck; Steffen Schotthöfer (CoRR, 2025)