Adaptive symplectic model order reduction of parametric particle-based Vlasov-Poisson equation

TL;DR

This paper develops an adaptive, structure-preserving reduced order model for parametric particle-based Vlasov-Poisson equations, significantly reducing computational costs while maintaining accuracy in multi-query plasma simulations.

Contribution

It introduces a novel nonlinear reduced basis approach with adaptive sampling and hyper-reduction techniques, including DMD and DEIM, to efficiently simulate parametric Hamiltonian systems.

Findings

Significant reduction in particle number for simulations

Efficient decoupling of nonlinear operator evaluations

Retention of Hamiltonian structure in reduced models

Abstract

High-resolution simulations of particle-based kinetic plasma models typically require a high number of particles and thus often become computationally intractable. This is exacerbated in multi-query simulations, where the problem depends on a set of parameters. In this work, we derive reduced order models for the semi-discrete Hamiltonian system resulting from a geometric particle-in-cell approximation of the parametric Vlasov-Poisson equations. Since the problem's non-dissipative and highly nonlinear nature makes it reducible only locally in time, we adopt a nonlinear reduced basis approach where the reduced phase space evolves in time. This strategy allows a significant reduction in the number of simulated particles, but the evaluation of the nonlinear operators associated with the Vlasov-Poisson coupling remains computationally expensive. We propose a novel reduction of the nonlinear terms that combines adaptive parameter sampling and hyper-reduction techniques to address this. The proposed approach allows decoupling the operations having a cost dependent on the number of particles from those that depend on the instances of the required parameters. In particular, in each time step, the electric potential is approximated via dynamic mode decomposition (DMD) and the particle-to-grid map via a discrete empirical interpolation method (DEIM). These approximations are constructed from data obtained from a past temporal window at a few selected values of the parameters to guarantee a computationally efficient adaptation. The resulting DMD-DEIM reduced dynamical system retains the Hamiltonian structure of the full model, provides good approximations of the solution, and can be solved at a reduced computational cost.