sdofmv2 0.1.0

Solar phenomena prediction models

SDO FM v2: [Full Title of the Project/Model]

Introduction

SDOFMv2 is an advanced multi-instrument foundation model designed to analyze Solar Dynamics Observatory (SDO) data and drive large-scale, data-driven heliophysics research. Building upon the original SDOFM framework, this version addresses previous limitations like restricted temporal coverage and reconstruction artifacts to significantly improve spatial coherence and global consistency.

A Masked Autoencoder (MAE) based on a Vision Transformer (ViT) architecture is utilized for pretraining. During this phase, a% of the image patches are masked, while the remaining (100 - a)% are processed by the encoder. The decoder block then reconstructs all patches, optimized via a customized loss function.

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Getting Started

Prerequisites

• Linux or macOS
• Python 3.11+
• NVIDIA GPU + CUDA toolkit (Recommended for training)

Environment Setup

We recommend using mamba to manage dependencies.

Important Hardware Note: > The sdofmv2_environment.yml file is configured for CUDA 12.8 by default. If your hardware or drivers require a different CUDA version (e.g., CUDA 11.8), please open sdofmv2_environment.yml and modify the pip section at the bottom to match your system (e.g., change cu128 to cu118) before running the setup commands.

Using Mamba:

# Clone the repository
git clone [https://github.com/Joaggi/sdofmv2.git](https://github.com/Joaggi/sdofmv2.git)
cd sdofmv2

# Create and activate the environment (This automatically installs PyTorch and the local package)
mamba env create -f sdofmv2_environment.yml
mamba activate sdofmv2

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Repository Structure

.
├── configs/                # YAML configurations for experiments
│   ├── downstream/         # Configs for downstream tasks (F10.7, solar wind)
│   └── pretrain/           # Configs for MAE pretraining (AIA, HMI)
├── notebooks/              # Jupyter notebooks for analysis and visualization
│   ├── analysis/           # Attention maps, PCA, and masking analysis
│   └── downstream_apps/    # How to use downstream scripts (Notebooks) for F10.7 and missing data applications
├── scripts/                # Executable scripts for training and testing
│   ├── pretrain.py         # Main pretraining script
│   ├── finetuning_*.py     # Scripts for downstream finetuning
│   └── test.py             # Script for evaluating checkpoints
├── src/                    # Core source code package
│   └── sdofmv2/
│       ├── core/           # Base model architectures and modules
│       ├── tasks/          # PyTorch Lightning modules (model & data module) for downstream tasks
│       └── utils/          # Helper functions, physical constants and metrics
├── pyproject.toml          # Project metadata and build dependencies
└── sdofmv2_environment.yml # Mamba environment definition file

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How to Use

(Note: It is recommended to run all scripts from the root directory of the repository so that file paths to configs/ and src/ resolve correctly.)

1. Data Preparation

Before training or running inference, you need to prepare the dataset. [Explain where to download the data, or provide a command if you have a script for it.]

python scripts/download_data_cache.py --target_dir ./assets/

2. Training the Model

To train the model from scratch, execute the pretraining script and pass the relevant configuration file.

python scripts/pretrain.py --config-name pretrain_mae_AIA.yaml

3. Inference and Evaluation

To evaluate a pre-trained checkpoint on the test set:

python scripts/test.py --config-name pretrain_mae_AIA.yaml

4. Downstream Finetuning

To finetune the model on a specific downstream task (e.g., solar wind forecasting):

python scripts/finetuning_solarwind.py --config-name finetune_solarwind_config.yaml

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Results & Visualizations

[Include a brief summary of the model's performance. You can add a table of metrics or a sample plot showing predictions vs. ground truth.]

The first row displays the original ground-truth images. The second and third rows show the model's reconstructed images using masking ratios of 0% and 50%, respectively.

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Citation

If you find this repository or model useful in your academic research, please consider citing our work:

@misc{sdofmv2,
  author = {Hong, Jinsu and Martin, Daniela and Gallego, Joseph},
  title = {SDOFMv2: A Multi-Instrument Foundation Model for the Solar Dynamics Observatory with Transferable Downstream Applications},
  year = {2026},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{[https://github.com/Joaggi/sdofmv2](https://github.com/Joaggi/sdofmv2)}},
  note = {Jinsu Hong, Daniela Martin, and Joseph Gallego contributed equally to this work}
}

Contributing

Contributions, bug reports, and feature requests are welcome! Please feel free to check the issues page or submit a pull request.