Huang Ziyu, Dong Chuanfei, Wang Liang
Department of Astronomy, Center for Space Physics, Boston University, Boston, MA 02215.
Proc Natl Acad Sci U S A. 2025 Mar 18;122(11):e2419073122. doi: 10.1073/pnas.2419073122. Epub 2025 Mar 12.
Nonlinear plasma physics problems are usually simulated through comprehensive modeling of phase space. The extreme computational cost of such simulations has motivated the development of multi-moment fluid models. However, a major challenge has been finding a suitable fluid closure for these fluid models. Recent developments in physics-informed machine learning have led to a renewed interest in constructing accurate fluid closure terms. In this study, we take an approach that integrates kinetic physics from the first-principles Vlasov simulations into a fluid model (through the heat flux closure term) using the Fourier neural operator-a neural network architecture. Without resolving the phase space dynamics, this new fluid model is capable of capturing the nonlinear evolution of the Landau damping process that exactly matches the Vlasov simulation results. This machine learning-assisted new approach provides a computationally affordable framework that surpasses previous fluid models in accurately modeling the kinetic evolution of complex plasma systems.
非线性等离子体物理问题通常通过相空间的综合建模来模拟。此类模拟极高的计算成本推动了多矩流体模型的发展。然而，一个主要挑战是为这些流体模型找到合适的流体闭合关系。基于物理的机器学习的最新进展引发了人们对构建精确流体闭合项的新兴趣。在本研究中，我们采用一种方法，利用傅里叶神经算子（一种神经网络架构）将来自第一性原理弗拉索夫模拟的动力学物理（通过热通量闭合项）集成到流体模型中。无需解析相空间动力学，这种新的流体模型就能捕捉与弗拉索夫模拟结果精确匹配的朗道阻尼过程的非线性演化。这种机器学习辅助的新方法提供了一个计算成本可承受的框架，在精确模拟复杂等离子体系统的动力学演化方面超越了以往的流体模型。
