Stochastic Galerkin particle methods for kinetic equations of plasmas with uncertainties
Andrea Medaglia, Lorenzo Pareschi, Mattia Zanella

TL;DR
This paper introduces a stochastic Galerkin particle method for kinetic plasma models with uncertainties, achieving spectral accuracy and preserving physical properties, suitable for complex plasma phenomena under uncertain conditions.
Contribution
A novel sG particle method for collisional plasma models that maintains physical properties and is asymptotic-preserving in the fluid limit.
Findings
Accurately captures plasma dynamics under uncertainties.
Preserves conservation laws and positivity.
Effective in various plasma simulation scenarios.
Abstract
The study of uncertainty propagation is of fundamental importance in plasma physics simulations. To this end, in the present work we propose a novel stochastic Galerkin (sG) particle {method} for collisional kinetic models of plasmas under the effect of uncertainties. This class of methods is based on a generalized polynomial chaos (gPC) expansion of the particles' position and velocity. In details, we introduce a stochastic particle approximation for the Vlasov-Poisson system with a BGK term describing plasma collisions. A careful reformulation of such dynamics is needed to perform the sG projection and to obtain the corresponding system for the gPC coefficients. We show that the sG particle method preserves the main physical properties of the problem, such as conservations and positivity of the solution, while achieving spectral accuracy for smooth solutions in the random space.…
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Taxonomy
TopicsStatistical Mechanics and Entropy · Markov Chains and Monte Carlo Methods · Probabilistic and Robust Engineering Design
