Efficient Neural Hybrid System Learning and Interpretable Transition System Abstraction for Dynamical Systems

This paper proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient method of dynamics learning and system identification. First, a low-level model is 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 is trained to abstract the low-level neural hybrid system model into a transition system that allows Computational Tree Logic (CTL) verification to promote the model's ability to handle human interaction and verification efficiency.