RII Track-4: Investigating Solar Wind-Magnetosphere Coupling

太阳驱动几乎所有的（中性和带电粒子）波动在近地空间或地球空间。太阳风携带的太阳能量通过一个称为磁重联的相互作用过程转移到地球磁场或磁层边缘的地球空间。这种能量转移造成的地球空间波动可能对关键的国家基础设施（如卫星、电网等）产生不利影响。危及宇航员的安全然而，我们还没有完全了解不同的太阳风条件如何影响转移到磁层的能量。该研究金将联合收割机结合洛斯阿拉莫斯国家实验室（LANL）的磁层专业知识和西弗吉尼亚大学（WVU）PI的数据科学/机器学习专业知识，为地球空间/磁层开发一个新的模型，这将有助于提高我们对这种能量转移过程的理解。该项目将通过访问LANL为PI和西弗吉尼亚大学的研究生提供一个独特的培训机会，并促进长期的合作伙伴关系，这将改善西弗吉尼亚州的研究和教育。地球空间环境是一个高度复杂的系统，由紧密耦合在一起的区域组成。这些区域经历了由太阳风能量输入引起的显著变化。能量转移主要通过一个称为磁重联的过程发生，该过程为几乎所有的物理和动力地球空间过程提供燃料。了解太阳风-磁层耦合对于建模和预测地球空间变化至关重要，这些变化可能威胁地球，空中和太空中的安全，基础设施和财产。拟议项目的目标是获得对太阳风-磁层耦合物理学的新见解。模拟研究可以提供宝贵的见解的过程中，然而，现有模型的影响是有限的，其相当大的计算成本。该项目将结合联合收割机在LANL的磁层专业知识和PI在物理信息机器学习方面的专业知识，开发一个降阶模型（ROM），该模型嵌入了完整物理模型的物理特性，但大大降低了计算成本。ROM将首次允许进行全面的敏感性和不确定性分析，以确定占主导地位的太阳风驱动因素。结合系统和全面的模式数据比较，ROM将揭示最重要的过程，甚至可能发现太阳风-磁层耦合中被忽视或缺失的元素。该项目将为一名研究生提供有价值的培训，并帮助西弗吉尼亚大学和LANL建立长期的合作伙伴关系，这将加强西弗吉尼亚州的研究和教育，使其更具竞争力。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。

The Sun drives almost all of the (neutral and charged particle) fluctuations in near Earth space or geospace. The energy from the Sun carried by solar wind is transferred to the geospace at the edge of the Earth's magnetic field or magnetosphere through an interaction process called magnetic reconnection. The geospace fluctuations caused by this energy transfer can adversely impact critical national infrastructure (e.g. satellites, power grids, etc.) and endanger the astronauts. However, we do not yet completely understand how the different solar wind conditions affect the amount of energy transferred into the magnetosphere. The fellowship will combine the magnetospheric expertise at the Los Alamos National Laboratory (LANL) with the data science/machine learning expertise of the PI at West Virginia University (WVU) to develop a new model for the geospace/magnetosphere that will help to improve our understanding of this energy transfer process. The project will provide a unique training opportunity for the PI and a graduate student at WVU through visits to LANL and foster long-term collaboration partnerships that will improve research and education in the state of West Virginia.The geospace environment is a highly complex system made up of regions that are closely coupled together. The regions undergo significant variations caused by energy input from the solar wind. The energy transfer occurs primarily through a process known as magnetic reconnection which fuels almost all of the physical and dynamical geospace processes. Understanding the solar wind-magnetosphere coupling is crucial to modeling and predicting the geospace variations that can threaten safety, infrastructure, and property on Earth, in the air, and in space. The goal of the proposed project is to gain new insights into the physics of solar wind-magnetosphere coupling. Simulation studies can provide invaluable insights into the process; however, the impact of existing models is limited by their considerable computational cost. The project will combine the magnetospheric expertise at LANL and PI's expertise in Physics-Informed Machine Learning to develop a reduced order model (ROM) that embeds the physics of the full physical model but significantly reduces the computational cost. The ROM will permit, for the first time, a comprehensive sensitivity and uncertainty analyses to identify the dominant solar wind drivers. Combined with systematic and comprehensive model-data comparisons, the ROM will reveal the paramount processes and may even discover overlooked or missing elements in the solar wind-magnetosphere coupling. The project will provide one graduate student valuable training and help develop a long-term partnership between WVU and LANL that will enhance research and education in the state of West Virginia and make it more competitive.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.