Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations. - Gelu Nita; Manolis K. Georgoulis; Irina Kitiashvili; Viacheslav Sadykov; Enrico Camporeale; Alexander Kosovichev; Haimin Wang; Vincent Oria; Jason T. L. Wang; Rafal A. Angryk; Berkay Aydin; Azim Ahmadzadeh; et al. (CoRR, 2020)

Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes. - Jack Ziegler; Ryan M. McGranaghan (ICMLA, 2021)

Risk Identification & Quantification in Complex Human-Natural Systems via Convergent Data Intensive Research. - Toryn L. J. Schafer; Ryan M. McGranaghan; Mila Getmansky Sherman; Mei-Ling E. Feng; Olukunle O. Owolabi; Sean E. Ryan; Marie-Christine Düker; Michael Jauch; David S. Matteson (KDD, 2021)

Towards a Modular Ontology for Space Weather Research. - Cogan Shimizu; Ryan M. McGranaghan; Aaron Eberhart; Adam C. Kellerman (WOP (Book), 2021)

Global Earth Magnetic Field Modeling and Forecasting with Spherical Harmonics Decomposition. - Panagiotis Tigas; Téo Bloch; Vishal Upendran; Banafsheh Ferdoushi; Mark C. M. Cheung; Siddha Ganju; Ryan M. McGranaghan; Yarin Gal; Asti Bhatt (CoRR, 2021)

Global geomagnetic perturbation forecasting using Deep Learning. - Vishal Upendran; Panagiotis Tigas; Banafsheh Ferdousi; Téo Bloch; Mark C. M. Cheung; Siddha Ganju; Asti Bhatt; Ryan M. McGranaghan; Yarin Gal (CoRR, 2022)

Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050. - Ryan M. McGranaghan; Barbara J. Thompson; Enrico Camporeale; J. Bortnik; Monica G. Bobra; G. Lapenta; S. Wing; Bala Poduval; S. Lotz; S. Murray; Michael S. F. Kirk; T. Y. Chen; et al. (CoRR, 2022)