We propose a novel learning framework for Koopman operator of nonlinear dynamical systems that is informed by the governing equation and guarantees long-time stability and robustness to noise. In contrast to existing frameworks where either ad-hoc observables or blackbox neural networks are used to construct observables in the extended dynamic mode decomposition (EDMD), our observables are informed by governing equations via Polyflow. To improve the noise robustness and guarantee long-term stability, we designed a stable parameterization of the Koopman operator together with a progressive learning strategy for roll-out recurrent loss. To further improve model performance in the phase space, a simple iterative strategy of data augmentation was developed. Numerical experiments of prediction and control of classic nonlinear systems with ablation study showed the effectiveness of the proposed techniques over several state-of-the-art practices.

翻译：本文提出了一种新颖的Koopman算子学习框架，该框架通过控制方程获取信息，并保证长期稳定性与噪声鲁棒性。与现有框架中采用临时观测函数或黑盒神经网络构建扩展动态模态分解（EDMD）观测量的方法不同，本框架通过Polyflow从控制方程中获取观测信息。为提升噪声鲁棒性并保证长期稳定性，我们设计了Koopman算子的稳定参数化方法，并结合渐进式学习策略处理滚动循环损失。为进一步提升模型在相空间中的性能，开发了简单的迭代数据增强策略。通过对经典非线性系统进行预测与控制的数值实验及消融研究，证明了所提技术相较于多种先进方法具有显著优势。