Researchers from the Institute of Science and Technology Austria, including Fabian Paischer, Gianluca Galletti, William Hornsby, Paul Setinek, Lorenzo Zanisi, Naomi Carey, Stanislas Pamela, and Johannes Brandstetter, have developed a novel approach to model plasma turbulence, a significant challenge in the pursuit of viable fusion power. Their work, published in the journal Nature Communications, introduces GyroSwin, a scalable neural surrogate designed to capture the complex dynamics of plasma turbulence more accurately and efficiently than current methods.
Plasma turbulence is a major obstacle in fusion energy production, as it impairs plasma confinement and complicates reactor design. The nonlinear gyrokinetic equation, which governs plasma turbulence, is computationally intensive due to its five-dimensional nature. As a result, reduced-order models are often used to approximate turbulent energy transport, but these models overlook important nonlinear effects.
GyroSwin addresses this issue by extending hierarchical Vision Transformers to five dimensions, enabling it to model the full nonlinear gyrokinetic simulations. The model introduces cross-attention and integration modules to facilitate interactions between electrostatic potential fields and the distribution function. Additionally, GyroSwin employs channelwise mode separation, inspired by nonlinear physics, to enhance its predictive capabilities.
The researchers demonstrated that GyroSwin outperforms widely used reduced numerics in predicting heat flux and capturing the turbulent energy cascade. Moreover, it reduces the computational cost of fully resolved nonlinear gyrokinetics by three orders of magnitude while maintaining physical verifiability. The model’s promising scaling laws, tested up to one billion parameters, suggest that it could pave the way for scalable neural surrogates in gyrokinetic simulations of plasma turbulence.
For the energy sector, GyroSwin’s ability to accurately model plasma turbulence and reduce computational costs could significantly advance fusion energy research. By providing more precise estimates of turbulent heat transport, GyroSwin can aid in the design and optimization of next-generation fusion reactors, bringing us closer to achieving reliable and sustainable fusion power.
Source: Nature Communications
This article is based on research available at arXiv.