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

• URL: http://arxiv.org/abs/2411.10240v1
• Date: Fri, 15 Nov 2024 14:53:34 GMT
• Title: Efficient Neural Hybrid System Learning and Transition System Abstraction for Dynamical Systems
• Authors: Yejiang Yang, Zihao Mo, Weiming Xiang,
• Abstract summary: A low-level model will be trained to learn the system dynamics. A high-level model will be trained to abstract the low-level neural hybrid system model.
• Score: 1.1470070927586018
• License:
• Abstract: 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.

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