Learning Reduced-Order Dynamics with Singularity via Latent-Augmented Neural Ordinary Differential Equations

2026-08-22

AI Summary

该论文针对工业降阶建模中相空间轨迹自交问题，提出了一种增强型神经常微分方程框架（LA-NODEs），通过增加潜在变量提高模型表达能力，从而更准确地表示冲突向量场，提升学习精度。

Abstract

This paper addresses the issue of self-intersecting trajectories (in phase space) in industrial reduced-order modeling and proposes the Latent-Augmented Neural Ordinary Differential Equations (LA-NODEs) framework. From the perspective of artificial intelligence, the proposed method augments conventional neural ordinary differential equations to enhance model expressiveness, enabling the representation of conflicting vector fields that may arise in reduced-order systems, thereby improving learning accuracy. Through theoretical analysis, the underlying mechanism of the framework is established, and a condition for determining the minimum required augmentation dimension is derived. From the perspective of engineering applications, the effectiveness of the proposed method is validated on the reduced-order system of two representative industrial models, namely an interior permanent magnet synchronous motor (IPMSM) drive and a distributed energy system (DES). Experimental results demonstrate that the proposed method can recover system features that are difficult to capture using conventional approaches and achieve superior performance in terms of prediction accuracy and modeling fidelity, thereby providing an effective approach for high-precision data-driven modeling of complex industrial systems.

Problem

• self-intersecting trajectories
• reduced-order modeling
• industrial systems

Innovation

• Latent-Augmented Neural ODEs
• Reduced-Order Modeling
• Conflicting Vector Fields
• Industrial Systems
• Model Expressiveness