论文标题
数据驱动的从全型模拟中发现减少的等离子体物理模型
Data-driven discovery of reduced plasma physics models from fully-kinetic simulations
论文作者
论文摘要
血浆物理学中一些最重要的问题的核心 - 从受控的核融合到宇宙射线的加速 - 是描述非线性，多尺度等离子体动力学的挑战。在准确性和复杂性之间平衡的减少血浆模型的发展对于推进理论理解并实现这些问题的整体计算描述至关重要。在这里，我们直接根据第一原理粒子中的粒子模拟报告了以偏微分方程的形式的数据驱动的数据驱动的发现，以偏微分方程的形式。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目标，并表明这对于在存在离散粒子噪声的情况下稳健地识别管理方程至关重要。我们通过恢复血浆物理模型的基本层次结构来证明这种方法的潜力 - 从弗拉索夫方程到磁流失动力学。我们的发现表明，这种数据驱动的方法提供了一种有希望的新途径，以加速复杂非线性血浆现象的理论模型的发展，并为多尺度等离子体模拟设计计算有效算法。
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, multi-scale 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 new route to accelerate the development of reduced theoretical models of complex nonlinear plasma phenomena and to design computationally efficient algorithms for multi-scale plasma simulations.
