AI Foundation Model for Heliophysics: Applications, Design, and Implementation

Deep learning-based methods have been widely researched in the areas of language and vision, demonstrating their capacity to understand long sequences of data and their usefulness in numerous helio-physics applications. Foundation models (FMs), which are pre-trained on a large-scale datasets, form t...

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Vydané v:	Nature astronomy
Hlavní autori:	Roy, Sujit, Singh, Talwinder, Freitag, Marcus, Schmude, Johannes, Lal, Rohit, Hegde, Dinesha, Ranjan, Soumya, Gaur, Vishal, Vos, Etienne Ebon, Ghosal, Rinki, Patro, Badri Narayana, Aydin, Berkay, Pogorelov, Nikolai, Moreno, Juan Bernabe, Maskey, Manil, Ramachandran, Rahul
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Marshall Space Flight Center Nature Astronomy 14.10.2024
Predmet:	Cybernetics, Artificial Intelligence and Robotics
ISSN:	2397-3366
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Popis
Shrnutí:	Deep learning-based methods have been widely researched in the areas of language and vision, demonstrating their capacity to understand long sequences of data and their usefulness in numerous helio-physics applications. Foundation models (FMs), which are pre-trained on a large-scale datasets, form the basis fora variety of downstream tasks. These models, especially those based on trans-formers in vision and language, show exceptional potential for adapting to a wide range of downstream applications. In this paper, we provide our perspective on the criteria for designing a FM for heliophysic and associated challenges and applications using the Solar Dynamics Observatory (SDO) dataset. We believe that this is the first study to design a foundation model in the domain of heliophysics.
Bibliografia:	Marshall Space Flight Center MSFC
ISSN:	2397-3366