A generative machine learning surrogate model of plasma turbulence

TL;DR

This paper introduces GAIT, a generative AI surrogate model for plasma turbulence that significantly accelerates long-term transport simulations while maintaining high accuracy in reproducing turbulence characteristics.

Contribution

The paper presents the first use of generative AI to create a surrogate model for plasma turbulence, combining auto-encoders and recurrent neural networks for fast, accurate simulations.

Findings

GAIT generates turbulence states 400 times faster than direct numerical methods.

The model accurately reproduces spectral and flow topology features of plasma turbulence.

GAIT successfully captures Lagrangian transport properties and diffusivity.

Abstract

Generative artificial intelligence methods are employed for the first time to construct a surrogate model for plasma turbulence that enables long time transport simulations. The proposed GAIT (Generative Artificial Intelligence Turbulence) model is based on the coupling of a convolutional variational auto-encoder, that encodes precomputed turbulence data into a reduced latent space, and a recurrent neural network and decoder that generates new turbulence states 400 times faster than the direct numerical integration. The model is applied to the Hasegawa-Wakatani (HW) plasma turbulence model, that is closely related to the quasigeostrophic model used in geophysical fluid dynamics. Very good agreement is found between the GAIT and the HW models in the spatio-temporal Fourier and Proper Orthogonal Decomposition spectra, and the flow topology characterized by the Okubo-Weiss decomposition. The GAIT model also reproduces Lagrangian transport including the probability distribution function of particle displacements and the effective turbulent diffusivity.