pith. sign in

arxiv: 2606.04878 · v1 · pith:ACVUTDITnew · submitted 2026-06-03 · ⚛️ physics.comp-ph · physics.plasm-ph

Sparse and low-rank kinetic distribution estimation

classification ⚛️ physics.comp-ph physics.plasm-ph
keywords kineticdistributionslow-rankmethodsproposeallowallowsapplied
0
0 comments X
read the original abstract

In this paper, we consider methods that allow for memory-efficient storage of high-dimensional distributions and retain certain key features thereof, specifically in a kinetic theory context. We propose an extension to the entropic quadrature method that allows for enforcing sparsity, and propose a new low-rank decomposition approach that ensures preservation of moment information. The methods are applied to several model kinetic distributions, as well as to distributions obtained from high-resolution kinetic simulations of the Vlasov--Maxwell system.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.