Denoising Particle-In-Cell data via Smoothness-Increasing Accuracy-Conserving filters with application to Bohm speed computation-Reference-Cited by-同舟云学术

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Denoising Particle-In-Cell data via Smoothness-Increasing Accuracy-Conserving filters with application to Bohm speed computation

Published:2024-04 Issue: Volume:502 Page:112790
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Picklo Matthew J.,Tang Qi,Zhang Yanzeng,Ryan Jennifer K.^ORCID,Tang Xian-Zhu

Funder

National Nuclear Security Administration

Advanced Scientific Computing Research

Fusion Energy Sciences

Los Alamos National Laboratory

U.S. Department of Energy

Air Force Office of Scientific Research

Publisher

Elsevier BV

Reference34 articles.

1. An unsupervised machine-learning checkpoint-restart algorithm using Gaussian mixtures for particle-in-cell simulations;Chen;J. Comput. Phys.,2021

2. Partially linearized algorithms in gyrokinetic particle simulation;Dimits;J. Comput. Phys.,1993

3. A fully nonlinear characteristic method for gyrokinetic simulation;Parker;Phys. Fluids, B Plasma Phys.,1993

4. Sparse grid techniques for particle-in-cell schemes;Ricketson;Plasma Phys. Control. Fusion,2016

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Characteristic Mapping Method for Vlasov–Poisson with Extreme Resolution Properties;Communications in Computational Physics;2024-06

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