PISCES Public Preview

PISCES is a physics-informed convolutional autoencoder for detecting anormal 60-minute solar-wind patterns near L1 Lagrange point. This preview includes our initial model, a lightweight runner, and example inputs. Full preprocessing, scoring, evaluation, training, and baselines would be released later in May 2026.

Files

• model/pisces_preview.pt — exported model
• pisces_preview/run_preview.py — preview runner
• examples/example_normalized_windows.csv — small synthetic example input
• examples/omni_may2024_preview_windows.csv — two normalized NASA OMNI windows
• examples/expected_preview_output.csv — expected output for the OMNI preview input
• presentation/Poster.pdf — poster PDF
• presentation/Poster.png — poster preview image for README
• presentation/Presentation.pdf — presentation
• Q&A.md — Q&A guide
• MODEL_CARD.md — model summary
• CITATION.cff — citation metadata
• CHECKSUMS.txt — SHA-256 checksums

Poster

Click the image to open the full poster.

Q&A

For details on what is included in the project, how to run it, and what will come with the full release, please refer Q&A.md.

Install

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Run

Synthetic example:

python -m pisces_preview.run_preview examples/example_normalized_windows.csv --out preview_output.csv

NASA OMNI preview windows:

python -m pisces_preview.run_preview examples/omni_may2024_preview_windows.csv --out omni_preview_output.csv

Expected OMNI output is in examples/expected_preview_output.csv.

Input format

Required columns:

window_id,step,bx,by,bz,bt,density,speed,temperature

Each window_id must have 60 rows with step values 0..59. Optional start_utc and source values must be constant within a window and are copied to the output.

Values are normalized model inputs. Raw OMNI download and preprocessing are planned for the full release.

Verify files

sha256sum -c CHECKSUMS.txt

Citation

Please cite the paper and star this repo if you find it useful. We will continue maintaining and updating this repo. Feel free to contact kevinlee69720@g.ucla.edu and alison.march@colorado.edu, or open an issue if you have any questions.

@inproceedings{march2026pisces,
  title={{PISCES}: Physics-Informed Convolutional Autoencoder for Solar Wind Anomaly
         Detection and Space Weather Early Warning},
  author={March, Alison J. and Lee, Kevin},
  booktitle={NASA 5th Eddy Cross-Disciplinary Symposium},
  year={2026},
  address={Boulder, Colorado}
}