A generative machine learning surrogate model of plasma turbulence - Institut Brestois du Numérique et des Mathématiques Access content directly
Preprints, Working Papers, ... Year : 2024

A generative machine learning surrogate model of plasma turbulence

Abstract

State-of-the-art techniques in generative artificial intelligence 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 reduce latent space, and a deep neural network and decoder that generate 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 as well as in the flow topology characterized by the Okubo-Weiss decomposition. Agreement is also found in the probability distribution function of particle displacements and the effective turbulent diffusivity.
Fichier principal
Vignette du fichier
AI_plasma_turbulence_model.pdf (1.12 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04600564 , version 1 (04-06-2024)

Identifiers

Cite

B Clavier, D Zarzoso, E Frénod, Diego Del-Castillo-Negrete. A generative machine learning surrogate model of plasma turbulence. 2024. ⟨hal-04600564⟩
75 View
19 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More