AI Foundation Model for Heliophysics: Applications, Design, and Implementation
This work addresses the need for a foundational AI model in heliophysics, which could benefit researchers by providing a powerful, adaptable tool for analyzing solar data.
This paper proposes a design framework for an AI Foundation Model specifically for heliophysics, leveraging deep learning and transformer architectures. The authors outline criteria, challenges, and potential applications using the Solar Dynamics Observatory (SDO) dataset, aiming to create a versatile model for various downstream tasks in the field.
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.