GyroSwin：用于回旋动理学等离子体湍流模拟的五维代理模型

GyroSwin: 5D Surrogates for Gyrokinetic Plasma Turbulence Simulations

October 8, 2025

作者: Fabian Paischer, Gianluca Galletti, William Hornsby, Paul Setinek, Lorenzo Zanisi, Naomi Carey, Stanislas Pamela, Johannes Brandstetter

cs.AI

摘要

核聚变在寻求可靠且可持续的能源生产中扮演着关键角色。实现可行聚变能源的一个主要障碍在于理解等离子体湍流，它严重影响了等离子体的约束，这对于下一代反应堆设计至关重要。等离子体湍流由非线性回旋动力学方程所支配，该方程随时间演化一个五维分布函数。由于其高昂的计算成本，实践中常采用降阶模型来近似湍流能量传输。然而，这些模型忽略了完整五维动力学特有的非线性效应。为解决这一问题，我们提出了GyroSwin，首个可扩展的五维神经代理模型，能够模拟五维非线性回旋动力学仿真，从而捕捉降阶模型忽略的物理现象，同时提供湍流热传输的精确估计。GyroSwin（i）将层次化视觉Transformer扩展至五维，（ii）引入了交叉注意力和积分模块，用于静电势场与分布函数之间的潜在三维⇄五维交互，（iii）执行通道模式分离，灵感源自非线性物理。我们证明，GyroSwin在热通量预测上优于广泛使用的降阶数值方法，捕捉了湍流能量级联，并将完全解析的非线性回旋动力学成本降低了三个数量级，同时保持物理可验证性。GyroSwin展示了良好的扩展规律，测试参数规模高达十亿，为等离子体湍流回旋动力学仿真的可扩展神经代理模型开辟了道路。

English

Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to viable fusion power is understanding plasma turbulence, which significantly impairs plasma confinement, and is vital for next-generation reactor design. Plasma turbulence is governed by the nonlinear gyrokinetic equation, which evolves a 5D distribution function over time. Due to its high computational cost, reduced-order models are often employed in practice to approximate turbulent transport of energy. However, they omit nonlinear effects unique to the full 5D dynamics. To tackle this, we introduce GyroSwin, the first scalable 5D neural surrogate that can model 5D nonlinear gyrokinetic simulations, thereby capturing the physical phenomena neglected by reduced models, while providing accurate estimates of turbulent heat transport.GyroSwin (i) extends hierarchical Vision Transformers to 5D, (ii) introduces cross-attention and integration modules for latent 3Dleftrightarrow5D interactions between electrostatic potential fields and the distribution function, and (iii) performs channelwise mode separation inspired by nonlinear physics. We demonstrate that GyroSwin outperforms widely used reduced numerics on heat flux prediction, captures the turbulent energy cascade, and reduces the cost of fully resolved nonlinear gyrokinetics by three orders of magnitude while remaining physically verifiable. GyroSwin shows promising scaling laws, tested up to one billion parameters, paving the way for scalable neural surrogates for gyrokinetic simulations of plasma turbulence.