SLAR method with implicit LoMaC correction and adaptive-weight projection conserves mass, momentum, and energy for the Vlasov-Poisson system up to 2D-2V while retaining large time steps and high-order accuracy.
Zheng et al.A Semi-Lagrangian adaptive-rank (SLAR) method for high-dimensional Vlasov dynamics
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper investigates the effects of time integrator selection, numerical dissipation, and problem representation on the efficiency and stability of quantized tensor train simulations for advection-dominated test problems.
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A Mass, Momentum, and Energy Conserving Semi-Lagrangian Adaptive-Rank (SLAR) Method for the Vlasov-Poisson System
SLAR method with implicit LoMaC correction and adaptive-weight projection conserves mass, momentum, and energy for the Vlasov-Poisson system up to 2D-2V while retaining large time steps and high-order accuracy.
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A practical investigation on time integration in the quantized tensor train format
The paper investigates the effects of time integrator selection, numerical dissipation, and problem representation on the efficiency and stability of quantized tensor train simulations for advection-dominated test problems.