Data-driven discovery of reduced plasma physics models from fully kinetic simulations
Data-driven discovery of reduced plasma physics models from fully kinetic simulations
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DOI：
10.1103/physrevresearch.4.033192
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发表时间：
2022-09-09
影响因子：
4.2
通讯作者：
Fiuza, F.
中科院分区：
文献类型：
--
作者：
Alves, E. P.;Fiuza, F.
等离子体物理学中一些最重要的问题--从受控的核聚变到宇宙射线的加速--的核心是描述非线性、多尺度等离子体动力学的挑战。在精确度和复杂性之间取得平衡的简化等离子体模型的开发，对于推进理论理解和实现对这些问题的整体计算描述至关重要。在这里，我们报道了直接从第一原理粒子单元模拟中以偏微分方程组的形式发现精确的简化等离子体模型的数据驱动的发现。我们通过使用基于稀疏性的模型发现技术的积分公式来实现这一点，并表明这对于在离散粒子噪声存在的情况下稳健地识别控制方程是至关重要的。我们通过恢复等离子体物理模型的基本层次--从Vlasov方程到磁流体动力学--展示了这种方法的潜力。我们的发现表明，这种数据驱动的方法为加速发展复杂的非线性等离子体现象的简化理论模型和设计计算高效的多尺度等离子体模拟算法提供了一条很有前途的途径。
At the core of some of the most important problems in plasma physics-from controlled nuclear fusion to the acceleration of cosmic rays-is the challenge to describe nonlinear, multiscale plasma dynamics. The development of reduced plasma models that balance between accuracy and complexity is critical to advancing theoretical comprehension and enabling holistic computational descriptions of these problems. Here we report the data-driven discovery of accurate reduced plasma models, in the form of partial differential equations, directly from first-principles particle-in-cell simulations. We achieve this by using an integral formulation of sparsity-based model-discovery techniques and show that this is crucial to robustly identify the governing equations in the presence of discrete particle noise. We demonstrate the potential of this approach by recovering the fundamental hierarchy of plasma physics models-from the Vlasov equation to magnetohydrodynamics. Our findings show that this data-driven methodology offers a promising route to accelerate the development of reduced theoretical models of complex nonlinear plasma phenomena and to design computationally efficient algorithms for multiscale plasma simulations.