力矩闭合
结束语(心理学)
朗道阻尼
非线性系统
计算流体力学
统计物理学
热流密度
物理
人工神经网络
计算机科学
力矩(物理)
相空间
流体力学
流体力学
经典力学
机械
传热
人工智能
湍流
量子力学
经济
市场经济
热力学
作者
Ziyu Huang,Chuanfei Dong,Liang Wang
标识
DOI:10.1073/pnas.2419073122
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI