Metadata-Version: 2.4
Name: seawrd
Version: 0.3.0
Summary: Surrogate Emulator for Aquatic World Radius Determination
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_docs
Requires-Dist: numpy
Requires-Dist: pandas

<p align="center"><img src="seawrd.jpg" alt="orbitize!" 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)
[![Documentation Status](https://readthedocs.org/projects/SEAWRD/badge/?version=latest)](http://seawrd.readthedocs.io/en/latest/?badge=latest)
![PyPI - Version](https://img.shields.io/pypi/v/seawrd)
[![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 is written in Python and was pursued as a part of Code/Astro Workshop 2026 by Group 13. The authors of this package are:

|Ashley Parr|Bishwash Devkota|Fredi Quisipe|Ian Rain-water|
|-----|----|----|-----|


## Attribution

Please cite the DOI if you make use of this software in your research. [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.20869608.svg)](https://doi.org/10.5281/zenodo.20869608)


## Acknowledgements

The authors of this project would like to thank [Artyom Aguichine](https://an0wen.github.io/) for providing the motivation and skeleton code adapted for this Code/Astro project.

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).
