seawrd 0.3.0

Surrogate Emulator for Aquatic World Radius Determination

SEAWRD

Surrogate Emulator for Aquatic World Radius Determination - "sea-ward"

Surrogate model creator for predicting the radius of irradiated ocean worlds. For installation instructions, tutorials, and detailed documentation, start here.

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 Surrogate Emulator for Aquatic World Radius Determination 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.

Acknowledgements

The authors of this project would like to thank Artyom Aguichine 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.