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arXiv 2604.03439physics.plasm-ph
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基于粒子-网格模拟的无分辨率机器学习热通量闭合

Resolution-Independent Machine Learning Heat Flux Closure for ICF Plasmas

University of Oxford(牛津大学);STFC Rutherford Appleton Laboratory(STFC鲁瑟福阿普顿实验室);Mach42

M. Luo, A. R. Bell, F. Miniati, S. M. Vinko, G. Gregori

AI总结:

本文提出一种无分辨率机器学习热通量闭合方法,通过傅里叶神经算子训练,实现了在不同空间和时间分辨率下的热通量预测,提升了数据驱动闭合在辐射-流体模拟中的实用性。

摘要:

准确建模惰性约束聚变等离子体中的热通量需要在远离局部平衡和不同空间时间分辨率下保持预测性的闭合。我们开发了一种基于粒子-网格模拟的无分辨率机器学习热通量闭合,使用傅里叶神经算子进行训练。两个非局部电子热传导模型被训练和测试。当自洽嵌入到电子能量方程中时,学习到的闭合能忠实再现温度演变,并表现出良好的时间外推和泛化能力。值得注意的是,用低分辨率数据训练的模型在部署到显著更细分辨率的隐式迭代求解器中时,能准确预测热通量,显著提升了将数据驱动闭合嵌入偏微分方程求解器的实用性。这些结果建立了一种数据驱动的闭合,连接了动能和流体描述,并为将机器学习作为迭代求解器应用于ICF等离子体的辐射-流体模拟提供了可行路径。

英文摘要:

Accurate modeling of heat flux in inertial confinement fusion plasmas requires closures that remain predictive far from local equilibrium and across disparate spatial and temporal resolutions. We develop a resolution-independent machine-learning heat flux closure trained on particle-in-cell simulations using a Fourier Neural Operator. Two nonlocal electron thermal conduction models are trained and tested. When embedded self-consistently into the electron energy equation, the learned closure faithfully reproduces the temperature evolution and shows good temporal extrapolation and generalization capability. Remarkably, models trained on coarse-resolution data accurately predict heat flux when deployed in substantially finer-resolution implicit, iterative solvers of the energy equation, significantly enhancing the practicality of embedding data-driven closures into partial differential equation solvers. These results establish a data-driven closure that bridges kinetic and fluid descriptions and provides a viable pathway for treating machine learning as an iterative solver within the radiation-hydrodynamic simulations of ICF plasma.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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