Learning Reduced-Order Dynamics with Singularity via Latent-Augmented Neural Ordinary Differential Equations
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 ...
| Authors | Xiaorui Wang, Yu Zhou, Wenjie Mei et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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