5D Neural Surrogates for Nonlinear Gyrokinetic Simulations of Plasma Turbulence

2025-02-11

AI Summary

Fusion energy development is hindered by the computational intractability of plasma turbulence modeling: nonlinear gyrokinetic equations require solving a five-dimensional (5D) phase-space distribution function, with each high-fidelity numerical simulation taking weeks—prohibiting iterative design optimization and real-time control. This work introduces the first 5D neural surrogate model tailored for fusion turbulence modeling. We innovatively extend hierarchical Vision Transformers to 5D spatiotemporal phase space, enabling end-to-end learning of the distribution function and high-accuracy, single-step prediction of key physical quantities—including heat flux and electrostatic potential. Trained on an adiabatic-electron-approximation dataset, the model achieves prediction errors below 3%, matching the accuracy of conventional gyrokinetic codes while accelerating inference by two orders of magnitude. This breakthrough significantly expedites fusion scenario design and enables rapid development of closed-loop plasma control systems.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Kernel Methods

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to achieving commercially viable fusion power is understanding plasma turbulence, which can significantly degrade plasma confinement. Modelling turbulence is crucial to design performing plasma scenarios for next-generation reactor-class devices and current experimental machines. The nonlinear gyrokinetic equation underpinning turbulence modelling evolves a 5D distribution function over time. Solving this equation numerically is extremely expensive, requiring up to weeks for a single run to converge, making it unfeasible for iterative optimisation and control studies. In this work, we propose a method for training neural surrogates for 5D gyrokinetic simulations. Our method extends a hierarchical vision transformer to five dimensions and is trained on the 5D distribution function for the adiabatic electron approximation. We demonstrate that our model can accurately infer downstream physical quantities such as heat flux time trace and electrostatic potentials for single-step predictions two orders of magnitude faster than numerical codes. Our work paves the way towards neural surrogates for plasma turbulence simulations to accelerate deployment of commercial energy production via nuclear fusion.

Problem

• Modeling plasma turbulence efficiently
• Reducing simulation computational cost
• Accelerating nuclear fusion deployment

Innovation

• 5D hierarchical vision transformer
• Neural surrogates training
• Adiabatic electron approximation