AI Foundation Model for Heliophysics: Applications, Design, and Implementation
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912073165832192 |
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| author | Roy, Sujit Singh, Talwinder Freitag, Marcus Schmude, Johannes Lal, Rohit Hegde, Dinesha Ranjan, Soumya Lin, Amy Gaur, Vishal Vos, Etienne Eben Ghosal, Rinki Patro, Badri Narayana Aydin, Berkay Pogorelov, Nikolai Moreno, Juan Bernabe Maskey, Manil Ramachandran, Rahul |
| author_facet | Roy, Sujit Singh, Talwinder Freitag, Marcus Schmude, Johannes Lal, Rohit Hegde, Dinesha Ranjan, Soumya Lin, Amy Gaur, Vishal Vos, Etienne Eben Ghosal, Rinki Patro, Badri Narayana Aydin, Berkay Pogorelov, Nikolai Moreno, Juan Bernabe Maskey, Manil Ramachandran, Rahul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10841 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI Foundation Model for Heliophysics: Applications, Design, and Implementation Roy, Sujit Singh, Talwinder Freitag, Marcus Schmude, Johannes Lal, Rohit Hegde, Dinesha Ranjan, Soumya Lin, Amy Gaur, Vishal Vos, Etienne Eben Ghosal, Rinki Patro, Badri Narayana Aydin, Berkay Pogorelov, Nikolai Moreno, Juan Bernabe Maskey, Manil Ramachandran, Rahul Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition 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. |
| title | AI Foundation Model for Heliophysics: Applications, Design, and Implementation |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.10841 |