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arXiv · 2607.25804

Horizon Selection in Physics-Enhanced Neural ODEs: Theoretical Insights and Flux Linkage Application

Giulio Montecchio·Benjamin Hartmann·Sven Reimann·Maximilian Manderla·Jan Achterhold·Daniel Görges
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Abstract

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.

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Giulio Montecchio, Benjamin Hartmann, Sven Reimann, Maximilian Manderla, Jan Achterhold, Daniel Görges. 2026-07-28. Horizon Selection in Physics-Enhanced Neural ODEs: Theoretical Insights and Flux Linkage Application. https://arxiv.org/abs/2607.25804

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