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

arXiv: https://arxiv.org/abs/2410.10841 · HTML: https://mlsanity.com/paper/2410.10841/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 80 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 30 of 31)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Masked Autoencoders As Spatiotemporal Learners](https://mlsanity.com/paper/2205.09113/) (2022)
- [ClimaX: A foundation model for weather and climate](https://mlsanity.com/paper/2301.10343/) (2023)
- [GraphCast: Learning skillful medium-range global weather forecasting](https://mlsanity.com/paper/2212.12794/) (2022)
- [PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers](https://mlsanity.com/paper/2308.05732/) (2023)
- [GenCast: Diffusion-based ensemble forecasting for medium-range weather](https://mlsanity.com/paper/2312.15796/) (2023)
- [DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting](https://mlsanity.com/paper/2306.01984/) (2023)
- [Long Short-Term Transformer for Online Action Detection](https://mlsanity.com/paper/2107.03377/) (2021)
- [SpectFormer: Frequency and Attention is what you need in a Vision Transformer](https://mlsanity.com/paper/2304.06446/) (2023)
- [Prithvi WxC: Foundation Model for Weather and Climate](https://mlsanity.com/paper/2409.13598/) (2024)
- [A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission](https://mlsanity.com/paper/1903.04538/) (2019)
- [AI Foundation Models for Weather and Climate: Applications, Design, and Implementation](https://mlsanity.com/paper/2309.10808/) (2023)
- [Enhancing SDO/HMI images using deep learning](https://mlsanity.com/paper/1706.02933/) (2017)
- [Machine learning in solar physics](https://mlsanity.com/paper/2306.15308/) (2023)
- [Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning](https://mlsanity.com/paper/2008.12080/) (2020)
- [Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics](https://mlsanity.com/paper/1905.13575/) (2019)
- [AtmoDist: Self-supervised Representation Learning for Atmospheric Dynamics](https://mlsanity.com/paper/2202.01897/) (2022)
- [Solar Coronal Magnetic Field Extrapolation from Synchronic Data with AI-generated Farside](https://mlsanity.com/paper/2010.07553/) (2020)
- [Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona](https://mlsanity.com/paper/1911.04006/) (2019)
- [High Resolution Solar Image Generation using Generative Adversarial Networks](https://mlsanity.com/paper/2106.03814/) (2021)
- [Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-Image Translation](https://mlsanity.com/paper/2208.09512/) (2022)
- [SCSS-Net: Solar Corona Structures Segmentation by Deep Learning](https://mlsanity.com/paper/2109.10834/) (2021)
- [A Spatial Agent-Based Model for Preemptive Evacuation Decisions During Typhoon](https://mlsanity.com/paper/2109.14459/) (2021)

## Built on by (most cited first, 3 of 3)

- [Surya: Foundation Model for Heliophysics](https://mlsanity.com/paper/2508.14112/) (2025)
- [SuryaBench: Benchmark Dataset for Advancing Machine Learning in Heliophysics and Space Weather Prediction](https://mlsanity.com/paper/2508.14107/) (2025)
- [Contrastive Heliophysical Image Pretraining for Solar Dynamics Observatory Records](https://mlsanity.com/paper/2511.22958/) (2025)

## 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.