# SLAMS

**Repository Path**: AI4EarthLab/SLAMS

## Basic Information

- **Project Name**: SLAMS
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-07-01
- **Last Updated**: 2025-07-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SLAMS: Score-based Latent Assimilation in Multimodal Setting

🎊🎊 __We won the best student paper award at CVPR EarthVision 2024!!__ 🎊🎊

📚 __Paper__: https://arxiv.org/abs/2404.06665

🔍 __Overview__: We recast data assimilation in a multimodal setting using a deep generative framework. In particular, we implement a latent score-based diffusion model. We project heterogeneous states and observations into a unified latent space where the forward and reverse conditional diffusion processes take place. Through varying ablation studies, given coarse, noisy, and sparse conditioning inputs, we find our method to be robust and physically consistent. Part of this implementation builds upon components originally developed by Rozet, F., & Louppe, G. [[paper](https://arxiv.org/abs/2306.10574), [code](https://github.com/francois-rozet/sda)] under the MIT License, which we have adapted and extended to fit our research framework.


## Quickstart
1. Install dependencies using `pip` or `conda`
```
pip install -r requirements.txt
```

2. Run sample notebooks under `notebooks/` marked with `01_` prefix. These examples are extended from [[1](https://github.com/francois-rozet/sda)] to benchmark against our latent approach.
- `a`: Lorenz'63 system
- `b`: Kolmogorov fluid

## Full Experiments
In order to reproduce the results in the paper, we have to acquire the necessary data:
1. Process the in-situ data `python process_cpc.py`
2. Process the ex-situ data `python process_noaa.py`
3. Process the ERA5 data from https://leap-stc.github.io/ChaosBench/quickstart.html, particularly
```
cd data/
wget https://huggingface.co/datasets/LEAP/ChaosBench/resolve/main/process.sh
chmod +x process.sh

./process.sh era5
./process.sh climatology
```

4. Update `slams/config.py` field: `ERA_DATADIR = <YOUR_ERA5_DIR>`, for instance `<PROJECT_DIR>/SLAMS/data`

5. All evaluations are summarized in a series of `notebooks/` marked with `02_` prefix.
    - `a`: Pixel-based data assimilation
    - `b`: Latent-based data assimilation NO observation (only background states)
    - `c`: Latent-based data assimilation with +1 observation (in-situ)
    - `d`: Latent-based data assimilation with +2 observation (in-situ + ex-situ)
    - `e`: Figures and tables generation


__NOTE:__ Training your own model is simple and is defined in `train_da.py`. First, define your latent model in `slams/nn.py` or score network in `slams/score.py`. Afterwards, unify both under `slams/model_da.py`. An example, as defined in the paper, has been provided for your reference.

## Citation
If you find any of the code useful, feel free to cite these works.
```
@misc{qu2024deep,
      title={Deep Generative Data Assimilation in Multimodal Setting}, 
      author={Yongquan Qu and Juan Nathaniel and Shuolin Li and Pierre Gentine},
      year={2024},
      eprint={2404.06665},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{rozet2024score,
  title={Score-based data assimilation},
  author={Rozet, Fran{\c{c}}ois and Louppe, Gilles},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}
```