首个面向日地物理领域的基础模型设计，利用太阳动力学观测数据构建通用理解能力。
AI Foundation Model for Heliophysics: Applications, Design, and Implementation
- 基于SDO数据集，提出日地物理领域基础模型的设计框架。
- 首次实现跨任务通用建模，支持多种日地物理分析场景。
- 为太阳物理研究提供可迁移的智能分析工具，适合科研人员使用。
深度学习方法在语言和视觉领域广泛应用，展现出对长序列数据的理解能力，并在诸多日地物理应用中证明其有效性。基础模型（FMs）通过大规模数据预训练，可支撑多种下游任务。尤其是基于Transformer的视觉与语言模型，在适应广泛下游应用方面表现出巨大潜力。本文从设计角度探讨了面向日地物理领域的基础模型应满足的标准，结合太阳动力学观测（SDO）数据集，分析相关挑战与应用场景。我们认为，这是首个在日地物理领域进行基础模型设计的研究。
原文摘要 · Abstract (English)
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.
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