GyroSwin：用于陀螺动力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

核聚變在追求可靠且可持續的能源生產中扮演著關鍵角色。實現可行核聚變能源的一個主要障礙是理解等離子體湍流，它嚴重影響等離子體的約束，並對下一代反應堆設計至關重要。等離子體湍流由非線性迴旋動力學方程控制，該方程隨時間演化一個五維分佈函數。由於其高昂的計算成本，實際中常採用降階模型來近似湍流能量輸運。然而，這些模型忽略了完整五維動力學特有的非線性效應。為解決這一問題，我們提出了GyroSwin，這是首個可擴展的五維神經代理模型，能夠模擬五維非線性迴旋動力學，從而捕捉降階模型所忽略的物理現象，同時提供湍流熱輸運的精確估計。GyroSwin（i）將分層視覺變換器擴展至五維，（ii）引入了交叉注意力與整合模塊，用於靜電勢場與分佈函數之間的潛在三維↔五維交互，（iii）基於非線性物理學啟發，執行通道模式分離。我們證明，GyroSwin在熱流預測上優於廣泛使用的降階數值方法，捕捉了湍流能量級聯，並將完全解析的非線性迴旋動力學成本降低了三個數量級，同時保持物理可驗證性。GyroSwin展現出有前景的擴展規律，測試參數高達十億，為等離子體湍流迴旋動力學模擬的可擴展神經代理模型鋪平了道路。

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.