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相关论文: A Local Macroscopic Conservative (LoMaC) low rank …

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In this paper, we propose a novel Local Macroscopic Conservative (LoMaC) low rank tensor method with discontinuous Galerkin (DG) discretization for the physical and phase spaces for simulating the Vlasov-Poisson (VP) system. The LoMaC…

数值分析 · 数学 2022-10-14 Wei Guo , Jannatul Ferdous Ema , Jing-Mei Qiu

In this paper, we propose a conservative low rank tensor method to approximate nonlinear Vlasov solutions. The low rank approach is based on our earlier work (arxiv: 2106.08834). It takes advantage of the fact that the differential…

数值分析 · 数学 2022-01-26 Wei Guo , Jing-Mei Qiu

The primary challenge in solving kinetic equations, such as the Vlasov equation, is the high-dimensional phase space. In this context, dynamical low-rank approximations have emerged as a promising way to reduce the high computational cost…

数值分析 · 数学 2021-07-28 Lukas Einkemmer , Ilon Joseph

Numerical methods that approximate the solution of the Vlasov-Poisson equation by a low-rank representation have been considered recently. These methods can be extremely effective from a computational point of view, but contrary to most…

数值分析 · 数学 2018-07-09 Lukas Einkemmer , Christian Lubich

We present a dynamical low-rank (DLR) method for the Vlasov-Poisson-Fokker-Planck (VPFP) system. Our main contributions are two-fold: (i) a conservative spatial discretization of the Fokker-Planck operator that factors into velocity-only…

数值分析 · 数学 2026-05-28 Shiheng Zhang , Jingwei Hu

Dynamical low-rank approximation, as has been demonstrated recently, can be extremely efficient in solving kinetic equations. However, a major deficiency is that they do not preserve the structure of the underlying physical problem. For…

数值分析 · 数学 2023-04-26 Lukas Einkemmer , Alexander Ostermann , Carmen Scalone

Kinetic equations are difficult to solve numerically due to their high dimensionality. A promising approach for reducing computational cost is the dynamical low-rank algorithm, which decouples the dimensions of the phase space by proposing…

数值分析 · 数学 2022-04-26 Jack Coughlin , Jingwei Hu

Structure-preserving geometric algorithm for the Vlasov-Maxwell (VM) equations is currently an active research topic. We show that spatially-discretized Hamiltonian systems for the VM equations admit a local energy conservation law in…

计算物理 · 物理学 2017-08-02 Jianyuan Xiao , Hong Qin , Jian Liu , Ruili Zhang

We introduce novel dynamical low-rank methods for solving large-scale matrix differential equations, motivated by algorithms from randomized numerical linear algebra. In terms of performance (cost and accuracy), our methods overperform…

数值分析 · 数学 2025-10-13 Benjamin Carrel

We develop a new method for proving hypocoercivity for a large class of linear kinetic equations with only one conservation law. Local mass conservation is assumed at the level of the collision kernel, while transport involves a confining…

偏微分方程分析 · 数学 2010-05-11 Jean Dolbeault , Clément Mouhot , Christian Schmeiser

This article is devoted to the Relativistic Vlasov-Maxwell system in space dimension three. We prove the local smooth solvability for weak topologies (and its long time version for small data). This result is derived from a representation…

偏微分方程分析 · 数学 2023-06-22 Christophe Cheverry , Slim Ibrahim

We present an energy-conserving numerical scheme to solve the Vlasov-Maxwell (VM) system based on the regularized moment method proposed in [Z. Cai, Y. Fan, and R. Li. CPAM, 2014]. The globally hyperbolic moment system is deduced for the…

数值分析 · 数学 2023-01-25 Tianai Yin , Xinghui Zhong , Yanli Wang

We propose a low-rank tensor approach to approximate linear transport and nonlinear Vlasov solutions and their associated flow maps. The approach takes advantage of the fact that the differential operators in the Vlasov equation is tensor…

数值分析 · 数学 2022-03-23 Wei Guo , Jing-Mei Qiu

We present a novel structure-preserving framework for solving the Vlasov-Poisson-Landau system of equations using a particle in cell (PIC) discretization combined with discrete gradient time integrators. The Vlasov-Poisson-Landau system is…

等离子体物理 · 物理学 2026-02-16 Daniel S. Finn , Joseph V. Pusztay , Matthew G. Knepley , Mark F. Adams

The action principle by Low [Proc. R. Soc. Lond. A 248, 282--287] for the classic Vlasov-Maxwell system contains a mix of Eulerian and Lagrangian variables. This renders the Noether analysis of reparametrization symmetries inconvenient,…

等离子体物理 · 物理学 2020-04-30 Eero Hirvijoki , Joshua W. Burby , David Pfefferlé , Alain J. Brizard

A numerical dynamical low-rank approximation (DLRA) scheme for the solution of the Vlasov-Poisson equation is presented. Based on the formulation of the DLRA equations as Friedrichs' systems in a continuous setting, it combines recently…

数值分析 · 数学 2025-08-15 André Uschmajew , Andreas Zeiser

This work proposes an adaptive structure-preserving model order reduction method for finite-dimensional parametrized Hamiltonian systems modeling non-dissipative phenomena. To overcome the slowly decaying Kolmogorov width typical of…

数值分析 · 数学 2022-02-02 Jan S. Hesthaven , Cecilia Pagliantini , Nicolò Ripamonti

Continuum Vlasov simulations can be utilized for highly accurate modelling of fully kinetic plasmas. Great progress has been made recently regarding the applicability of the method in realistic plasma configurations. However, a reduction of…

等离子体物理 · 物理学 2022-09-28 Florian Allmann-Rahn , Rainer Grauer , Katharina Kormann

This work presents a low-rank tensor model for multi-dimensional Markov chains. A common approach to simplify the dynamical behavior of a Markov chain is to impose low-rankness on the transition probability matrix. Inspired by the success…

系统与控制 · 电气工程与系统科学 2024-11-05 Madeline Navarro , Sergio Rozada , Antonio G. Marques , Santiago Segarra

In this study, we consider the numerical solution of large systems of linear equations obtained from the stochastic Galerkin formulation of stochastic partial differential equations. We propose an iterative algorithm that exploits the…

数值分析 · 数学 2016-05-18 Kookjin Lee , Howard C. Elman
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