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A fast Petrov-Galerkin spectral method for the multi-dimensional Boltzmann equation using mapped Chebyshev functions

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abstract

Numerical approximation of the Boltzmann equation presents a challenging problem due to its high-dimensional, nonlinear, and nonlocal collision operator. Among the deterministic methods, the Fourier-Galerkin spectral method stands out for its relative high accuracy and possibility of being accelerated by the fast Fourier transform. However, this method requires a domain truncation which is unphysical since the collision operator is defined in $\mathbb{R}^d$. In this paper, we introduce a Petrov-Galerkin spectral method for the Boltzmann equation in the unbounded domain. The basis functions (both test and trial functions) are carefully chosen mapped Chebyshev functions to obtain desired convergence and conservation properties. Furthermore, thanks to the close relationship of the Chebyshev functions and the Fourier cosine series, we are able to construct a fast algorithm with the help of the non-uniform fast Fourier transform (NUFFT). We demonstrate the superior accuracy of the proposed method in comparison to the Fourier spectral method through a series of 2D and 3D examples.

fields

cs.LG 1

years

2025 1

verdicts

CONDITIONAL 1

representative citing papers

Degree-Optimized Cumulative Polynomial Kolmogorov-Arnold Networks

cs.LG · 2025-05-21 · conditional · novelty 5.0

CP-KAN reformulates polynomial degree selection in Chebyshev-based Kolmogorov-Arnold networks as a QUBO optimization problem and shows competitive regression performance with fewer parameters on several benchmarks.

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  • Degree-Optimized Cumulative Polynomial Kolmogorov-Arnold Networks cs.LG · 2025-05-21 · conditional · none · ref 10 · internal anchor

    CP-KAN reformulates polynomial degree selection in Chebyshev-based Kolmogorov-Arnold networks as a QUBO optimization problem and shows competitive regression performance with fewer parameters on several benchmarks.