Collaborative Research: CyberTraining: Pilot: Cyberinfrastructure-Enabled Machine Learning for Understanding and Forecasting Space Weather

合作研究：网络培训：试点：网络基础设施支持的机器学习用于理解和预测空间天气

• 批准号: 2320148
• 负责人: Katherine Herbert
• 金额: $ 4.7万
• 依托单位: Montclair State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320148&HistoricalAwards=false
• 关键词: Collaborative; Research; CyberTraining; Pilot; Cyberinfrastructure

Space weather (SWx) refers to the transients in the space environment traveling from the Sun to Earth. SWx affects the life of human beings, including communication, transportation, power supplies, national defense, space travel, and more. In the recent decade, tackling the difficult task of understanding and forecasting violent solar eruptions, which are sources of SWx, and their terrestrial impacts has become a strategic national priority. Cyberinfrastructure (CI) is an extremely important part of SWx research, as many terabytes of data are generated daily from different sources. This collaborative project between New Jersey Institute of Technology (NJIT) and Montclair State University (MSU) builds upon a National Science Foundation funded CI platform for sharing CI enabled machine learning (ML) methods, tools, and resources for SWx data exploration and event prediction. The project incorporates the skills and lessons learned from the development of the NSF funded CI platform into a course curriculum. By transforming research results and findings into teaching modules, the project trains potential ML professionals to develop advanced CI enabled methods for understanding, monitoring, and forecasting SWx. Both NJIT and MSU are minority serving institutions with ample resources to support underrepresented students. Experienced project leaders oversee diversity, equity, and inclusion efforts for the project development.This project makes contributions to CI training by (1) developing learning modules for a new computer science graduate course, (2) providing students with opportunities to gain hands on experience in implementing ML solutions for SWx problems, (3) exposing students to advances in machine learning as a service, operational near real time SWx forecasting systems, and predictive intelligence with Binder enabled Zenodo archived open source ML tools, and (4) assessing the teaching and mentoring methods using formative and summative approaches. The principal investigators work with undergraduate students to develop CI resources and improve the sustainability of CI enabled ML tools. SWx has a profound impact on the Earth system. Building the SWx readiness merits substantial efforts on several fronts, including research, forecast, and mitigation plan. The new course nourishes graduate students, preparing them to become CI professionals capable of contributing to SWx monitoring and predictive analytics in general. The project provides training of the workforce in SWx research, which is critically important in many areas such as safety of space programs, radio communications and power grids. Knowledge generated from the project also has broader applications in other areas of science. The program, while small and pilot, can help address the need of CI professionals in New Jersey.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Astronomical Sciences within the NSF Directorate for Math and Physical Sciences (MPS) and the Division of Research, Innovation, Synergies, and Education (RISE) within the NSF Directorate for Geosciences (GEO).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.

太空天气（SWX）是指从太阳到地球传播的太空环境中的瞬变。 SWX影响人类的生活，包括沟通，运输，电力供应，国防，太空旅行等。在最近的十年中，应对理解和预测暴力太阳喷发的艰巨任务，这些爆发是SWX的来源，其陆地影响已成为战略性的国家优先事项。网络基础结构（CI）是SWX研究的极其重要的一部分，因为许多数据的数据都是从不同来源生成的。新泽西理工学院（NJIT）与蒙特克莱州立大学（MSU）之间的合作项目建立在国家科学基金会资助的CI CI平台，用于共享CI支持CI的机器学习（ML）方法，用于SWX数据探索和事件预测的方法，工具和资源。该项目结合了从NSF资助的CI平台的开发中学到的技能和经验教训。通过将研究结果和发现转换为教学模块，该项目训练潜在的ML专业人员开发启用CI的高级CI方法，以理解，监测和预测SWX。 NJIT和MSU都是少数派服务机构，拥有足够的资源来支持代表性不足的学生。经验丰富的项目领导者监督项目开发的多样性，公平和包容性工作。该项目通过（1）为新的计算机科学研究生课程开发学习模块为CI培训做出了贡献，（2）为学生提供实现SWX问题的经验的机会存档开源ML工具，以及（4）使用形成性和总结性方法评估教学和指导方法。主要研究人员与本科生合作开发CI资源并提高启用CI的ML工具的可持续性。 SWX对地球系统产生了深远的影响。在包括研究，预测和缓解计划在内的多个方面，建立SWX的准备工作值得大力努力。新课程滋养研究生，使他们成为CI专业人员，能够为SWX监测和预测分析做出贡献。该项目提供了SWX研究中的劳动力培训，这在许多领域（例如太空计划，无线电通信和电网的安全）中至关重要。该项目产生的知识在其他科学领域也具有更广泛的应用。该计划虽然小规模和飞行员可以帮助满足新泽西州CI专业人员的需求。 NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。