Physics-informed structured learning of a class of recurrent neural networks with guaranteed properties
Systems and Control
2026-03-27 v1 Systems and Control
Abstract
This paper proposes a physics-informed learning framework for a class of recurrent neural networks tailored to large-scale and networked systems. The approach aims to learn control-oriented models that preserve the structural and stability properties of the plant. The learning algorithm is formulated as a convex optimisation problem, allowing the inclusion of linear matrix inequality constraints to enforce desired system features. Furthermore, when the plant exhibits structural modularity, the resulting optimisation problem can be parallelised, requiring communication only among neighbouring subsystems. Simulation results show the effectiveness of the proposed approach.
Keywords
Cite
@article{arxiv.2603.25574,
title = {Physics-informed structured learning of a class of recurrent neural networks with guaranteed properties},
author = {Daniele Ravasio and Claudia Sbardi and Marcello Farina and Andrea Ballarino},
journal= {arXiv preprint arXiv:2603.25574},
year = {2026}
}