Physics Attention Transformer Surrogate for Rapid Vertical Instability Growth Rate Prediction: Alcator C-Mod to SPARC

2026-08-25

AI Summary

研究使用Physics Attention Transformer快速预测C-Mod和SPARC中垂直不稳定性增长率，以解决非刚性自由边界响应模型速度慢的问题。

Abstract

In this work, we investigate rapid prediction of the dominant $n{=}0$ vertical instability growth rate in C-Mod and SPARC equilibria, where nonrigid free boundary response models are too slow for control cycle use. Using a Physics Attention Transformer trained on MEQ-FGE-L labels, we predict both the scalar growth rate and the associated two dimensional perturbed toroidal current density. We find mean absolute errors of 5.4~s$^{-1}$ on held out C-Mod equilibria and 12.7~s$^{-1}$ on synthetic SPARC cases, with spatial eigenfunction errors near 5\%. We also compared PAT with operator based ML models : FNO2D and DeepONet, where we found PAT predicts a much lower normalised growth rate error and improved spatial reconstruction. These results indicate that PAT can reproduce MEQ-FGE-L outputs at control relevant latency and could support future studies of growth rate headroom monitoring and proximity aware shape control.

Problem

• vertical instability
• growth rate
• plasma equilibrium
• control cycle

Innovation

• Physics Attention Transformer
• vertical instability growth rate
• rapid prediction
• control cycle use
• spatial eigenfunction