A novel class of high-order uniformly accurate exponential integrators with local linear extension for the charged-particle dynamics under strong magnetic field
Year: 2026
Type: preprint
Abstract: In this paper, we develop a novel class of high-order uniformly accurate exponential integrators for charged-particle dynamics under strong magnetic field. The small parameter $0<\varepsilon\ll 1$ induces rapid temporal oscillations, rendering traditional numerical methods prohibitively expensive due to severe step-size restrictions. To address this issue, a linearization technology that introduces auxiliary polynomial variables is employed to recast the original charged-particle dynamics as a higher-dimensional system. Classical exponential integrators are subsequently applied to this augmented formulation, which inherently carries richer structural information, thereby yielding a family of uniformly accurate exponential integrators that can reach arbitrarily high order without requiring any order conditions. For strong magnetic fields under the maximal-ordering scaling, we rigorously establish through algebraic techniques that the schemes with auxiliary polynomial variables of degree $k\geq2$ achieve an improved position error bound of $\mathcal{O}(\min\{\varepsilon^{k+1}h,\varepsilon h^{k+1}\})$ and a uniform velocity error bound of $\mathcal{O}(\min\{\varepsilon^{k}h,h^{k+1}\})$, where $h$ denotes the time-step size. Under the additional condition that the magnetic field gradient vanishes at the origin, these bounds sharpen to $\mathcal{O}(\min\{\varepsilon^{k+2},\varepsilon h^{k+1}\})$ and $\mathcal{O}(\min\{\varepsilon^{k+1},h^{k+1}\})$ for the position and velocity, respectively, for $k\geq1$. The same sharper bounds hold for constant strong magnetic fields. Numerical experiments confirm the advantages of the proposed methods.
Source: arXiv (Cornell University)
Authors: Wang, Lina, Wang, Bin, Zhu, Beibei
Language: English
Subfield: Numerical Analysis
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: G2217401733, G7497052723
OpenAlex ID: W7163589346
