collaborators

10 papers

math.NA2026

A Dynamical Low-rank Multilevel Monte Carlo Estimator for High-Dimensional Kinetic Equations

Chinmay Patwardhan, Sebastian Krumscheid, Jonas Kusch +2

Kinetic equations are used to model a wide range of phenomena important for real-world applications. Their applications span astrophysics, nuclear physics, engineering, and social…

math.NA2026

An energy stable and conservative multiplicative dynamical low-rank discretization for the Su-Olson problem

Lena Baumann, Lukas Einkemmer, Christian Klingenberg +1

Computing numerical solutions of the thermal radiative transfer equations on a finely resolved grid can be costly due to high computational and memory requirements. A numerical red…

cs.LG2025

A geometric framework for momentum-based optimizers for low-rank training

Steffen Schotthöfer, Timon Klein, Jonas Kusch

Low-rank pre-training and fine-tuning have recently emerged as promising techniques for reducing the computational and storage costs of large neural networks. Training low-rank par…

math.NA2025

A high-order deterministic dynamical low-rank method for proton transport in heterogeneous media

Pia Stammer, Niklas Wahl, Jonas Kusch +1

Dose calculations in proton therapy require the fast and accurate solution of a high-dimensional transport equation for a large number of (pencil) beams with different energies and…

math.NA2025

An adaptive dynamical low-rank optimizer for solving kinetic parameter identification inverse problems

Lena Baumann, Lukas Einkemmer, Christian Klingenberg +1

The numerical solution of parameter identification inverse problems for kinetic equations can exhibit high computational and memory costs. In this paper, we propose a dynamical low…

math.NA2025

Low-rank variance reduction for uncertain radiative transfer with control variates

Chinmay Patwardhan, Pia Stammer, Emil Løvbak +2

The radiative transfer equation models various physical processes ranging from plasma simulations to radiation therapy. In practice, these phenomena are often subject to uncertaint…