Exploring helical dynamos with machine learning: Regularized linear regression outperforms ensemble methods
Journal article, 2019

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.


magnetohydrodynamics (MHD)



Farrukh Nauman

Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics

Joonas Nattila

Stockholm University

Royal Institute of Technology (KTH)

Astronomy and Astrophysics

0004-6361 (ISSN) 1432-0746 (eISSN)

Vol. 629 A89

Subject Categories

Computational Mathematics

Astronomy, Astrophysics and Cosmology



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