APTT: An accuracy-preserved tensor-train method for the Boltzmann-BGK equation

TL;DR

The paper introduces APTT, a tensor-train based numerical method that efficiently solves the Boltzmann-BGK equation while preserving accuracy, significantly reducing computational costs compared to traditional methods.

Contribution

The paper develops an accuracy-preserved tensor-train (APTT) method using low-rank tensor approximations and TT-MALS solver for efficient Boltzmann-BGK equation solutions.

Findings

Achieves significant reduction in computational and memory costs.

Maintains the same convergence rate as finite difference schemes.

Validated by benchmark test cases demonstrating efficiency and accuracy.

Abstract

Solving the Boltzmann-BGK equation with traditional numerical methods suffers from high computational and memory costs due to the curse of dimensionality. In this paper, we propose a novel accuracy-preserved tensor-train (APTT) method to efficiently solve the Boltzmann-BGK equation. A second-order finite difference scheme is applied to discretize the Boltzmann-BGK equation, resulting in a tensor algebraic system at each time step. Based on the low-rank TT representation, the tensor algebraic system is then approximated as a TT-based low-rank system, which is efficiently solved using the TT-modified alternating least-squares (TT-MALS) solver. Thanks to the low-rank TT representation, the APTT method can significantly reduce the computational and memory costs compared to traditional numerical methods. Theoretical analysis demonstrates that the APTT method maintains the same convergence rate as that of the finite difference scheme. The convergence rate and efficiency of the APTT method are validated by several benchmark test cases.