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arXiv · 2606.04878

Sparse and low-rank kinetic distribution estimation

Georgii Oblapenko·Lambert Theisen·Rostislav-Paul Wilhelm·Michael Herty·Manuel Torrilhon
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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.

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Georgii Oblapenko, Lambert Theisen, Rostislav-Paul Wilhelm, Michael Herty, Manuel Torrilhon. 2026-06-03. Sparse and low-rank kinetic distribution estimation. https://arxiv.org/abs/2606.04878

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