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

TL;DR

This paper introduces the first foundation model tailored for heliophysics, leveraging deep learning and transformer architectures to enhance data analysis and applications using the SDO dataset.

Contribution

It presents the design criteria, challenges, and potential applications of a novel heliophysics foundation model based on transformer technology.

Findings

Proposes a new FM architecture for heliophysics

Addresses domain-specific challenges in model design

Highlights potential applications with SDO data

Abstract

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.