This paper proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient way of dynamics learning and system identification. First, a low-level model will be trained to learn the system dynamics, which utilizes multiple simple neural networks to approximate the local dynamics generated from data-driven partitions. Then, based on the low-level model, a high-level model will be trained to abstract the low-level neural hybrid system model into a transition system that allows Computational Tree Logic Verification to promote the model's ability with human interaction and verification efficiency.

翻译：本文提出一种用于动态学习的神经网络混合建模框架，以促进可解释、计算高效的动态学习与系统辨识方法。首先，训练底层模型以学习系统动态，该模型利用多个简单神经网络来逼近数据驱动分区生成的局部动态。随后，基于底层模型，将训练高层模型以将底层神经混合系统模型抽象为转移系统，从而支持计算树逻辑验证，以提升模型在人机交互与验证效率方面的能力。