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eess.SY2026
Physics-informed structured learning of a class of recurrent neural networks with guaranteed properties
Daniele Ravasio, Claudia Sbardi, Marcello Farina +1
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…
eess.SY2026
A new approach for combined model class selection and parameters learning for auto-regressive neural models
Corrado Sgadari, Alessio La Bella, Marcello Farina
This work introduces a novel approach for the joint selection of model structure and parameter learning for nonlinear dynamical systems identification. Focusing on a specific Recur…