# Exploring helical dynamos with machine learning

## TL;DR

This paper applies machine learning techniques to analyze magnetic field evolution in helical MHD turbulence, finding that simple linear models are often sufficient and providing open access to data and tools.

## Contribution

It demonstrates that linear regression effectively predicts electromotive force in helical MHD turbulence, showing limited benefit from more complex models, and offers open data for further research.

## Key findings

- Linear regression predicts EMF well in the studied turbulence.
- More sophisticated algorithms do not significantly improve predictions.
- Data appears to be low-dimensional and effectively linear.

## Abstract

We use ensemble machine learning algorithms to study the evolution of magnetic fields in magnetohydrodynamic (MHD) turbulence that is helically forced. We perform direct numerical simulations of helically forced turbulence using mean field formalism, with electromotive force (EMF) modeled both as a linear and non-linear function of the mean magnetic field and current density. The form of the EMF is determined using regularized linear regression and random forests. We also compare various analytical models to the data using Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling. Our results demonstrate that linear regression is largely successful at predicting the EMF and the use of more sophisticated algorithms (random forests, MCMC) do not lead to significant improvement in the fits. We conclude that the data we are looking at is effectively low dimensional and essentially linear. Finally, to encourage further exploration by the community, we provide all of our simulation data and analysis scripts as open source IPython notebooks.