Generative-machine-learning surrogate model of plasma turbulence.

Area of Science:

• Plasma Physics
• Computational Fluid Dynamics
• Artificial Intelligence

Background:

• Plasma turbulence simulations are computationally intensive.
• Accurate long-time transport modeling is crucial for fusion energy research.
• Existing methods face limitations in speed and efficiency.

Purpose of the Study:

• To develop a novel surrogate model for plasma turbulence using generative AI.
• To enable significantly faster long-time transport simulations.
• To validate the model's accuracy against established plasma physics models.

Main Methods:

• Coupling a convolutional variational autoencoder with a recurrent neural network and decoder.
• Encoding precomputed turbulence data into a reduced latent space.
• Generating new turbulence states via deep learning.

Main Results:

• The Generative Artificial Intelligence Turbulence (GAIT) model achieved 400x acceleration compared to direct numerical integration.
• Excellent agreement was observed between GAIT and the Hasegawa-Wakatani model in spectral and topological analyses.
• GAIT accurately reproduced Lagrangian transport properties, including particle displacement distributions and effective turbulent diffusivity.

Conclusions:

• Generative AI offers a powerful tool for accelerating complex plasma physics simulations.
• The GAIT model provides a computationally efficient and accurate alternative for long-time transport studies.
• This AI-driven approach has significant implications for fusion energy research and geophysical fluid dynamics.