AI 中文总结
研究物理增强神经常微分方程训练中积分视界的作用，基于经典系统辨识得出视界扩展结论，提出利用更长视界减少偏差、提取信息及提升泛化能力的框架，并应用于永磁同步电机模型学习以联合估计磁链图和电阻。
AI 中文摘要
训练过程中的积分视界在物理增强神经常微分方程中起着关键作用。我们基于经典输入输出模型的非线性系统辨识，得出神经常微分方程训练中视界扩展的结论。据此，我们提出一个框架，利用更长视界减少物理参数估计偏差，从数据中提取残余信息并作为正则化器提升泛化能力。在永磁同步电机模型学习中，该方法用于联合估计磁链图和电阻。
英文摘要
The integration horizon during the training plays a critical role in Physics-Enhanced Neural Ordinary Differential Equations. We draw conclusions about horizon extension in the training of Neural Ordinary Differential Equations based on classical nonlinear system identification of input-output models. In light of this insight, we propose a framework that exploits longer horizons to reduce bias in physical parameter estimates, extracts residual information from data, and acts as a regularizer improving generalization. In the learning of a model for permanent magnet synchronous machine, the method is used to jointly estimate the flux map and the resistance.
CommentsAccepted for presentation at IFAC World Congress 2026
