planet-equil 0.1.0

PlaNet: reconstruction of plasma equilibrium and separatrix using convolutional physics-informed neural operator. See https://doi.org/10.1016/j.fusengdes.2024.114193

PlaNet: plasma equilibrium reconstruction using physics-informed neural operator.

This is the official repository if the planet package. It is a PyTorch implementation of PlaNet (PLAsma equilibrium reconstruction NETwork), a convolutional physics-informed neural operator for performing plasma equilibrium reconstruction using magnetic and non-magnetic measurements.

For any kind of reference on the model architecture or the mathematical formulation, see this paper. The original work was developed using TensorFlow, as mentioned in the paper. This is a polished and optimized version, using modern PyTorch and PyTorch Lightning.

Installation

First, create a virtual environment using venv

python3.10 -m venv venv 
source venv/bin/activate

then install the package and all the dependencies using poetry

pip3 install poetry==1.8.3
poetry config virtualenvs.create false
poetry install

Data

To train and test PlaNet you can use the dataset available at this repo, containing ~85k equilibria of an ITER-like devices. All the equilibria have been computed numerically using the free-boundary Grad-Shafranov solver FRIDA, which is publicly available here.

Tutorials

There are tutorial notebooks available to get started with planet:

• 1_dataset_creation.ipynb: show how to create and format your data to perform trainign and inference using the PlaNet model.
• 2_model_training.ipynb: shows how to perfrom a full training of the PlaNet model using PyTorch Lightning`.
• 3_load_pretrained_and_prediction.ipynb: shows how to load a pretrained model and how to use if to perform reconstruction of plasma equilibrium and to estimate the Grad-Shafranov operator.