Deep learning algorithms provide a new paradigm to study high-dimensional
dynamical behaviors, such as those in fusion plasma systems. Development of
novel model reduction methods, coupled with detection of abnormal modes with
plasma physics, opens a unique opportunity for building efficient models to
identify plasma instabilities for real-time control. Our Fusion Transfer
Learning (FTL) model demonstrates success in reconstructing nonlinear kink mode
structures by learning from a limited amount of nonlinear simulation data. The
knowledge transfer process leverages a pre-trained neural encoder-decoder
network, initially trained on linear simulations, to effectively capture
nonlinear dynamics. The low-dimensional embeddings extract the coherent
structures of interest, while preserving the inherent dynamics of the complex
system. Experimental results highlight FTL's capacity to capture transitional
behaviors and dynamical features in plasma dynamics -- a task often challenging
for conventional methods. The model developed in this study is generalizable
and can be extended broadly through transfer learning to address various
magnetohydrodynamics (MHD) modes.

利用深度学习算法研究高维度动力行为的新方法为等离子体聚变系统内的异常模式检测和建立高效模型来进行实时控制提供了独特机会，研究发现我们的 Fusion Transfer Learning (FTL) 模型通过学习有限数量的非线性模拟数据，成功重建出非线性可刚性模式结构，实验结果突出了 FTL 捕捉等离子体动力学的过渡行为和动力学特点的能力，并指出该模型是可推广的，并可通过迁移学习扩展以应对各种磁流体力学 (MHD) 模式。