5 papers
Directional Conformal Uncertainty Quantification from Learned Model Discrepancy
Cesare Donati, Fabrizio Dabbene, Martina Mammarella
We propose a conformal prediction framework for quantifying the error of physics-based predictors used in control, where simple models are preferred for synthesis, certification, a…
Identification of contractive Lur'e-type systems via kernel-based Lipschitz design
Cesare Donati, Fabrizio Dabbene, Constantino Lagoa +2
This paper addresses the problem of identifying contractive Lur'e-type systems. Specifically, it proposes an identification framework that integrates linear prior knowledge with a…
A kernel-based approach to physics-informed nonlinear system identification
Cesare Donati, Martina Mammarella, Giuseppe C. Calafiore +3
This paper presents a kernel-based framework for physics-informed nonlinear system identification. The key contribution is a structured methodology that extends kernel-based techni…
Recovering nonlinear dynamics from non-uniform observations: A physics-based identification approach with practical case studies
Cesare Donati, Martina Mammarella, Fabrizio Dabbene +2
Uniform and smooth data collection is often infeasible in real-world scenarios. In this paper, we propose an identification framework to effectively handle the so-called non-unifor…
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…