Use of Tensor-Train Decompositions with a Discrete Velocity Boltzmann Solver

Georgii Oblapenko

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arXiv · 2303.15142

Use of Tensor-Train Decompositions with a Discrete Velocity Boltzmann Solver

Georgii Oblapenko

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Abstract

In the present work, the Tensor-Train decomposition algorithm is applied to reduce the memory footprint of a stochastic discrete velocity solver for rarefied gas dynamics simulation. An energy-conserving modification to the algorithm is proposed, along with an interleaved collision/convection routine which allows for easy application of higher-order convection schemes. The performance of the developed algorithm is analyzed for several 0- and 1-dimensional model problems in terms of solution error and reduction in memory use requirements.

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Georgii Oblapenko. 2023-03-27. Use of Tensor-Train Decompositions with a Discrete Velocity Boltzmann Solver. https://arxiv.org/abs/2303.15142

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