RII Track-4: Investigating Solar Wind-Magnetosphere Coupling

太阳几乎驱动了近地空间或地球空间中所有(中性和带电粒子)的波动。太阳风携带的来自太阳的能量通过一种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。这种能量转移引起的地球空间波动可能会对关键的国家基础设施(如卫星、电网等)产生不利影响。并危及宇航员的安全。然而，我们还不完全了解不同的太阳风条件如何影响转移到磁层的能量。该研究金将把洛斯阿拉莫斯国家实验室(LANL)的磁层专门知识与西弗吉尼亚大学(WVU)国际和平研究所的数据科学/机器学习专门知识结合起来，开发一个新的地球空间/磁层模型，这将有助于增进我们对这一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一名研究生提供独特的培训机会，并促进长期合作伙伴关系，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由紧密相连的区域组成的高度复杂的系统。由于太阳风的能量输入，这些地区经历了显著的变化。能量转移主要通过一种称为磁重联的过程进行，该过程为几乎所有的物理和动力学地球空间过程提供燃料。了解太阳风-磁层耦合对于模拟和预测地球空间变化至关重要，这些变化可能威胁到地球、空中和太空的安全、基础设施和财产。拟议项目的目标是对太阳风-磁层耦合的物理学有新的见解。模拟研究可以提供对这一过程的宝贵见解；然而，现有模型的影响因其巨大的计算成本而受到限制。该项目将结合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，开发一种降阶模型(ROM)，该模型嵌入完整物理模型的物理，但显著降低计算成本。该光盘将首次允许进行全面的敏感性和不确定性分析，以确定主要的太阳风能驱动因素。结合系统和全面的模型数据比较，只读存储器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遗漏的元素。该项目将为一名研究生提供宝贵的培训，并帮助发展西弗吉尼亚州大学和LANL之间的长期合作伙伴关系，以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，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.