❗ An improved version of this model is available at https://github.com/philippbaumeister/ExoMDN ❗
This repository contains the trained machine learning models and python notebooks for the paper Machine-learning inference of the interior structure of low-mass exoplanets (Baumeister et al. 2020).
This project requires Python 3.
keras = 2.2.4numpy = 1.18.0scipy >= 1.2.0matplotlib >= 3.0.2tensorflow = 1.15.2tensorflow-probability = 0.7.0ipywidgets >= 7.4.2joblib >= 0.13.2scikit-learn = 0.22.1
conda env create -f requirements.yml
Activate with
conda activate tf1.15
pip install -r requirements.txt
- MDN_exoplanets.ipynb contains all the code to load the trained MDN models and predict the distribution of possible interior structures of a planet.
- The mdn directory contains the MDN layer code adopted from https://github.com/cpmpercussion/keras-mdn-layer.
- The models directory contains data scalers and the MDN models trained either with mass and radius of the planet, or with mass, radius, and k2.
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