Physics-informed structured learning of a class of recurrent neural networks with guaranteed properties
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908915771375616 |
|---|---|
| author | Ravasio, Daniele Sbardi, Claudia Farina, Marcello Ballarino, Andrea |
| author_facet | Ravasio, Daniele Sbardi, Claudia Farina, Marcello Ballarino, Andrea |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25574 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physics-informed structured learning of a class of recurrent neural networks with guaranteed properties Ravasio, Daniele Sbardi, Claudia Farina, Marcello Ballarino, Andrea Systems and Control 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. |
| title | Physics-informed structured learning of a class of recurrent neural networks with guaranteed properties |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2603.25574 |