深度学习模型可预测训练数据之外的混沌系统动态。
Deep Learning of the Evolution Operator Enables Forecasting of Out-of-Training Dynamics in Chaotic Systems
- 通过学习系统数学规律而非模仿数据，实现泛化预测。
- 在参数超出训练范围时仍能零样本预测混沌行为。
- 适合研究复杂系统中罕见事件与非训练状态的建模。
我们证明了深度学习模拟器可预测训练数据中不存在的混沌系统现象。基于Kuramoto-Sivashinsky模型和beta-plane湍流模型，评估了模拟器在多种场景下的表现：预测自发再层流、捕捉任意混沌初态、零样本预测训练参数范围外的动力学，以及从人为限制的训练数据集中刻画动力学统计特性。结果表明，深度学习模拟器可通过学习底层数学规律，揭示复杂系统中的涌现行为与稀有事件，而不仅限于模仿已观测模式。
原文摘要 · Abstract (English)
We demonstrate that a deep learning emulator for chaotic systems can forecast phenomena absent from training data. Using the Kuramoto-Sivashinsky and beta-plane turbulence models, we evaluate the emulator through scenarios probing the fundamental phenomena of both systems: forecasting spontaneous relaminarisation, capturing initialisation of arbitrary chaotic states, zero-shot prediction of dynamics with parameter values outside of the training range, and characterisation of dynamical statistics from artificially restricted training datasets. Our results show that deep learning emulators can uncover emergent behaviours and rare events in complex systems by learning underlying mathematical rules, rather than merely mimicking observed patterns.
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