Collaborative Research: GEM--Towards Developing Physics-informed Subgrid Models for Geospace MagnetoHydroDynamics (MHD) Simulations

在模拟太阳风和磁层系统之间的相互作用时，科学家们通常使用数值磁流体力学（MHD），这是一种导电流体模型，将所有互穿粒子物种作为一个单一的连续介质。越来越多的，MHD模型需要非常高的数值分辨率，为现实的全球磁层模拟的多尺度等离子体流。为了解决这个问题，该项目将为现有的全球磁层MHD模型开发新的参数化，并通过物理信息机器学习和随机建模进行数据驱动的发现。该项目将支持一名早期职业科学家担任高级人员职务。更广泛的主要影响将是改进全球磁层模型的磁流体动力学组成部分，从而改进空间天气的建模和预测。所发展的技术是非常普遍的，可以适用于其他复杂的高维动力系统，对其他科学和工程领域也有好处。为了扩大结果并证明其鲁棒性，GAMERA将采用一个物理问题层次来对几种类型的多尺度湍流MHD流进行动力学模拟，通过增加参考数据的复杂性来系统地提升：（1）Orszag-Tang涡旋的二维模拟，（2）Kelvin-Helmholtz不稳定性的二维模拟，（3）近地磁尾爆发性整体流动的三维模拟。将从基准高分辨率GAMERA模式解诊断以下关键时空参考数据：（一）次网格（小尺度）和大尺度场的分布，（二）包含大尺度场诱导反馈的次网格尺度强迫。物理信息机器学习和随机建模将用于开发亚网格尺度和诱导强迫的预测模型，并与粗尺度GAMERA模拟的大尺度流动相结合。开发的子网格尺度参数化的技能将通过与实际应用相关的一套全面的物理信息度量标准进行正式和系统的评估。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

While simulating the interaction between the solar wind and magnetosphere system, scientists usually use numerical magnetohydrodynamics (MHD), a model of electrically conducting fluids that treats all interpenetrating particle species together as a single continuous medium. Increasingly, MHD models require very-high numerical resolution for realistic global magnetosphere simulations of multiscale plasma flows. To address this problem, this project will develop new parameterizations for an existing global magnetosphere MHD model with the data-driven discovery by physics-informed machine learning and stochastic modeling. The project will support an earlier career scientist in a senior personnel role. The main broader impact will be the improvement of MHD components of global magnetosphere models, leading to better modeling and prediction of space weather. The developed techniques are very general and can be adapted to other complex high-dimensional dynamical systems with benefits to other areas of science and engineering.To broaden the results and prove their robustness, a hierarchy of physical problems will be employed for dynamical simulations of several types of multiscale turbulent MHD flows by GAMERA, to ascend systematically by increasing the reference data complexity: (1) 2D simulation of Orszag-Tang vortex, (2) 2D simulation of the Kelvin-Helmholtz instability, (3) 3D simulation of bursty bulk flows in the near-Earth magnetotail. The following key spatiotemporal reference data will be diagnosed from benchmark high-resolution GAMERA model solutions: (i) distributions of subgrid (small-scale) and large-scale fields, (ii) subgrid-scale forcing that encapsulates induced feedbacks on the large-scale fields. Physics-informed machine learning and stochastic modeling will be used to develop prognostic models of subgrid-scales and induced forcing, coupled to large-scale flow simulated by the coarse-scale GAMERA. Skills of the developed subgrid-scale parameterizations will be formally and systematically evaluated by the comprehensive set of physics-informed metrics relevant to practical applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.