机器学习的热流封闭用于非线性兰道减压的多时刻流体建模
Ziyu Huang1, Chuanfei Dong1, Liang Wang1
1Department of Astronomy, Center for Space Physics, Boston University, Boston, MA 02215.
概括
这项研究引入了一种新的机器学习辅助流体模型,用于非线性等离子体物理学. 它准确地捕捉了动力等离子体演变,就像兰道减压,为相位空间模拟提供了一个计算效率高的替代方案.
科学领域:
- 等离子体物理学的物理学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 模拟非线性等离子体物理学通常需要计算上昂贵的相位空间建模.
- 现有的多时刻流体模型在精确的流体关闭条件上扎.
- 基于物理的机器学习为开发精确的流体封闭提供了一个有希望的途径.
研究的目的:
- 为非线性等离子体物理学开发一个可负担的流体模型.
- 将动力物理学集成到流体模型中,使用机器学习进行准确的关闭.
- 准确地捕捉动力等离子体现象的非线性演变.
主要方法:
- 利用福利埃神经运算符,神经网络架构,将动力物理学集成到流体模型中.
- 专注于开发一个合适的热流闭合术语,由第一原则Vlasov模拟提供信息.
- 开发了一种机器学习辅助的流体模型,该模型无法解决相位空间动态.
主要成果:
- 新的流体模型准确地捕捉了兰道缓过程的非线性演变.
- 流体模型的结果与从Vlasov模拟中获得的结果完全一致.
- 与之前的流体模型相比,该模型在模拟动力等离子体演变方面表现出卓越的准确性.
结论:
- 机器学习,特别是神经操作员,可以有效地导出精确的流体关闭术语.
- 这种方法为模拟复杂的等离子体系统提供了一个计算高效的框架.
- 开发的模型为非线性等离子体物理学的传统相位模拟提供了一个可行的替代方案.
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