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

arXiv: https://arxiv.org/abs/2411.10240 · HTML: https://mlsanity.com/paper/2411.10240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 17 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Abstract (arXiv)

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.