6 citations · 12 across the 8 of their papers we have counts for
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eess.SY2024
A scalable, gradient-stable approach to multi-step, nonlinear system identification using first-order methods
Cesare Donati, Martina Mammarella, Fabrizio Dabbene +2
This paper presents three main contributions to the field of multi-step system identification. First, drawing inspiration from Neural Network (NN) training, it introduces a tool fo…
eess.SY2024★ 6 cited
Combining Off-White and Sparse Black Models in Multi-step Physics-based Systems Identification -- EXTENDED VERSION
Cesare Donati, Martina Mammarella, Fabrizio Dabbene +2
In this paper, we propose a unified framework for identifying interpretable nonlinear dynamical models that preserve physical properties. The proposed approach integrates physical…