A hierarchical sparse-grid particle method for the Vlasov--Poisson system

Abstract

We introduce a hierarchical sparse-grid (HSG) particle method for the numerical solution of the Vlasov--Poisson system. Sparse-grid PIC methods have so far been formulated within finite-difference frameworks, most notably through the sparse-grid combination technique (SGCT), which ties them to tensor-product Cartesian grids and globally defined component grids. This paper brings sparse-grid particle methods into the Galerkin setting: the field equation is solved in variational form on a hierarchical sparse-grid space spanned by B-splines of arbitrary degree, and the traditional charge deposition step is replaced by a direct Galerkin projection of the raw Monte Carlo density estimator onto this space. Beyond preserving the mesh-complexity and noise-reduction benefits of sparse grids, this reformulation substantially extends the versatility of the approach, opening the way to spatial adaptivity and to non-rectangular geometries, which are notoriously difficult to accommodate within the SGCT framework. We carry out a probabilistic error analysis decomposing the numerical error into a grid-based bias and a statistical noise component. Under mixed-derivative regularity assumptions on the particle distribution, the bias of the charge density in the $\mathrm{L}^2$-norm is shown to scale as $\mathcal{O}(h^{p+1}|\log h|^{d-1})$, where $p$ is the B-spline degree and $d$ the spatial dimension, and the statistical error in the $\mathrm{L}^1$-norm as $\mathcal{O}(|\log h|^{(d-1)/2}(Nh)^{-1/2})$, matching the accuracy of high-order SGCT-PIC methods. Corresponding bounds are derived for the electric field. The theoretical estimates are validated on classical kinetic plasma benchmarks, including configurations with limited regularity and strong anisotropies that are known to be challenging for sparse-PIC approximations.