用分段神经网络建模动态系统，再抽象为可验证的有限状态机。
Efficient Neural Hybrid System Learning and Transition System Abstraction for Dynamical Systems
- 用多个简单神经网络分区域拟合系统局部动态。
- 将神经混合模型抽象为可形式化验证的转移系统。
- 适合需要可解释性与安全验证的复杂系统建模场景。
本文提出一种神经网络混合建模框架，用于动力系统学习，旨在实现可解释且计算高效的系统辨识。首先，训练一个低层模型，利用多个简单的神经网络，基于数据驱动的分区来逼近局部系统动态；随后，基于该低层模型，训练一个高层模型，将低层神经混合系统抽象为转移系统，支持计算树逻辑验证，从而提升模型的人机交互能力与验证效率。
原文摘要 · Abstract (English)
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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