CyberTraining: Pilot: Interdisciplinary Workforce Training for Data-intensive Solar Astroinformatics

Space weather events such as solar flares, coronal mass ejections, and geomagnetic storms pose serious and wide-ranging risks to modern society. The impacts of these phenomena extend across a broad spectrum of stakeholders, from satellite operators and aviation authorities to power grid managers, GPS-dependent precision agriculture, emergency response agencies, and national defense systems. Protecting these critical sectors requires not only improved forecasting capabilities but also a well-trained scientific workforce capable of developing and applying advanced computational and data-driven methods to complex heliophysics problems. Despite the growing national priority placed on space weather preparedness, most students entering heliophysics and related fields lack formal training in the computational and data science methods essential for modern space weather research. This project addresses that gap by creating freely available, high-quality training materials that will reach students and early-career researchers across the country, regardless of the resources available at their home institutions. By making these materials openly accessible, the project expands access to cutting-edge cyberinfrastructure training and helps grow a skilled workforce prepared to address one of the most consequential scientific and societal challenges of our time. Through targeted training in scientific data management, artificial intelligence and machine learning, and high-performance computing for physical simulations, the project aims to prepare the next generation of researchers with the interdisciplinary skills needed to advance heliophysics and space weather science. This Pilot CyberTraining project develops interdisciplinary training materials and courses for undergraduate and graduate students at the intersection of computer science, data science, and heliophysics. This project will design and pilot three courses on astroinformatics, computational heliophysics, and machine learning for space weather prediction. These courses target three core cyberinfrastructure skill areas: scientific data management for heliophysics datasets, data-driven analytics including machine learning and deep learning, and high-performance computing for physical simulations. To train students and early-career researchers in the use of novel cyberinfrastructure tools, the project will also develop two interactive books covering practical applications in heliophysics foundation models and inner heliospheric magnetohydrodynamic simulations. Course and tutorial development will be guided by a structured, outcomes-driven design process, with project-based learning as the core pedagogical approach. All materials will be released as open educational resources and disseminated broadly to the space weather and heliophysics research and education community. Hands-on tutorials and educational materials will be provided to guide students and educators in accessing and using computational resources, with materials designed for deployment across a range of institutional and national high-performance computing environments. By integrating interdisciplinary cyberinfrastructure training with hands-on experience in heliophysics foundation modeling, AI-driven space weather prediction, and high-performance magnetohydrodynamic simulations, the program aims to support data-enabled heliophysics research and establish a skilled, interdisciplinary workforce capable of advancing both the science and the operational needs of the space weather community. 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. NSF Award ID: 2612498 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Berkay Aydin | Institution: Georgia State University Research Foundation, Inc., ATLANTA, GA | Award Amount: $299,979 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2612498 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2612498.html