Metadata-Version: 2.4
Name: seawrd
Version: 0.5.0
Summary: Surrogate Emulator for Aquatic World Radius Determination
Author: Artem Aguichine, Anne Dattilo, Hailey Feller
Author-email: Ashley Parr <amparr83@gmail.com>, Bishwash Devkota <bishwashdevkota567@gmail.com>, Fredi Quispe Huaynasi <fredifqh@gmail.com>, Ian Rain-Water <irainw@stanford.edu>
License-Expression: MIT
Project-URL: Repository, https://github.com/Siphlygon/SEAWRD
Description-Content-Type: text/markdown
Requires-Dist: keras
Requires-Dist: tensorflow
Requires-Dist: tensorflow_docs
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: tomli
Requires-Dist: tomli_w
Requires-Dist: matplotlib
Requires-Dist: pytest
Requires-Dist: pytest-cov
Requires-Dist: ipykernel
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Requires-Dist: pytest-cov; extra == "test"

<p align="center"><img src="seawrd.jpg" alt="seawrd" width="100"/></p>

# SEAWRD
**S**urrogate **E**mulator for **A**quatic **W**orld **R**adius **D**etermination - "sea-ward"

Surrogate model creator for predicting the radius of irradiated ocean worlds. For installation instructions, tutorials, and detailed documentation, start [here](http://seawrd.readthedocs.io).

![Build Status](https://github.com/Siphlygon/SEAWRD/actions/workflows/python-package.yml/badge.svg)
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[![A rectangular badge, half black half purple containing the text made at Code Astro](https://img.shields.io/badge/Made%20at-Code/Astro-blueviolet.svg)](https://semaphorep.github.io/codeastro/)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.20869608.svg)](https://doi.org/10.5281/zenodo.20869608)
![GitHub License](https://img.shields.io/github/license/Siphlygon/SEAWRD)


## Motivation

Planetary interior modelling for ocean worlds is a computationally demanding exercise, involving a lot of hydrodynamical considerations dependent on the composition and physical properties of a given exoplanet. This can take a number of minutes per-planet, which grows to be incredibly large when performing hundreds of thousands of simulations.

A cheap approximation is available in the form of surrogate models. A neural network can act as a general function learner, i.e., something that maps inputs to outputs, and so we can use pre-ran expensive simulation data to train a small neural network to reproduce the simulation's results with great accuracy in a fraction of the time.

This is what **S**urrogate **E**mulator for **A**quatic **W**orld **R**adius **D**etermination is for! Based on user-provided hyperparameters and data, it can train an appropriate surrogate model to be used as an approximation for the full hydrodynamical simulations, namely as a predictor of the radius of the planet.


## Contributors

SEAWRD as an open-source project was first pursued as a part of Code/Astro Workshop 2026 by Group 13, with members:
|[Ashley Parr](https://github.com/Siphlygon)|[Bishwash Devkota](https://github.com/bishwashdevkota)|[Fredi Quisipe](https://github.com/fredifqh)|[Ian Rain-Water](https://github.com/Irainw)|
|-----|----|----|-----|

The code, concepts, configuration, and set-up for the start of SEAWRD came from the following contributors, who are therefore equal authors of this project:
|[Artyom Aguichine](https://github.com/an0wen)|[Anne Dattilo](https://github.com/aedattilo)|[Hailey Feller](https://github.com/hfeller24)|
|-----|----|----|


## Attribution

Please cite the DOI if you make use of this software in your research. [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.20869607.svg)](https://doi.org/10.5281/zenodo.20869607)


## Acknowledgements

The data source file "DNN_data_IOP_Aguichine2021.dat" used in the example usage Jupyter notebook is from Aguichine et al. (2021), and you are encouraged to read their paper found here:

A. Aguichine, O. Mousis, M. Deleuil, and E. Marcq, “Mass–Radius relationships for irradiated ocean planets,” The Astrophysical Journal, vol. 914, no. 2, p. 84, Jun. 2021, doi: [10.3847/1538-4357/abfa99](https://doi.org/10.3847/1538-4357/abfa99).
