AI Foundation Model for Heliophysics: Applications, Design, and Implementation
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Abstract (arXiv)
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 for a variety of downstream tasks. These models, especially those based on transformers 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 an FM for heliophysics and associated challenges and applications using the Solar Dynamics Observatory (SDO) dataset. We believe that this is the first study to design an FM in the domain of heliophysics.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 31 of its 80 references that are in the map. First posted on arXiv 30 Sep 2024.
Builds on
31 of 80 references.
References · 31
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19 more
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