用5维神经网络加速等离子体湍流模拟，提速100倍。
5D Neural Surrogates for Nonlinear Gyrokinetic Simulations of Plasma Turbulence
- 扩展视觉变换器至5维，建模等离子体分布函数。
- 单步预测速度比传统代码快100倍，精度高。
- 适合融合能研发、仿真优化与控制研究者使用。
核聚变在实现可靠可持续能源生产中具有关键作用。实现商业化的聚变能源面临的主要障碍是理解等离子体湍流，其会显著降低等离子体约束性能。建模湍流对设计下一代反应堆级装置和现有实验设备的高性能等离子体场景至关重要。非线性回旋动力学方程是湍流建模的基础，其演化一个5维分布函数。数值求解该方程极为昂贵，单次运行需数周才能收敛，难以用于迭代优化与控制研究。本文提出一种训练5维回旋动力学模拟神经代理模型的方法。我们扩展了分层视觉变换器至五维，并在绝热电子近似下的5维分布函数上进行训练。结果表明，该模型可准确预测下游物理量（如热通量时间序列与静电势），单步预测速度比数值代码快两个数量级。本工作为发展等离子体湍流模拟的神经代理模型铺平道路，有望加速核聚变商业化能源的部署。
原文摘要 · Abstract (English)
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.
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