6 citations · 13 across the 13 of their papers we have counts for
12 papers · 1 filter
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…
A Model-based Approach for Glucose Control via Physical Activity
Pierluigi Francesco De Paola, Alessandro Borri, Alessia Paglialonga +2
The role played by physical activity in slowing down the progression of type-2 diabetes is well recognized. However, except for general clinical guidelines, quantitative real-time…
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 control system framework for counterfactuals: an optimization based approach
Pierluigi Francesco De Paola, Jared Miller, Alessandro Borri +2
Counterfactuals are a concept inherited from the field of logic and in general attain to the existence of causal relations between sentences or events. In particular, this concept…
Closed-loop robust control of long-term diabetes progression via physical activity management
Pierluigi Francesco De Paola, Alessandro Borri, Fabrizio Dabbene +2
Large clinical evidence acknowledges the crucial role played by physical activity in delaying the progression of type-2 diabetes. However, the literature lacks control approaches t…
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…
Measured-state conditioned recursive feasibility for stochastic model predictive control
Mirko Fiacchini, Martina Mammarella, Fabrizio Dabbene
In this paper, we address the problem of designing stochastic model predictive control (MPC) schemes for linear systems affected by unbounded disturbances. The contribution of the…
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…
A novel mathematical model for predicting the benefits of physical activity on type 2 diabetes progression
Pierluigi Francesco De Paola, Alessandro Borri, Fabrizio Dabbene +3
Despite the well-acknowledged benefits of physical activity for type 2 diabetes (T2D) prevention, the literature surprisingly lacks validated models able to predict the long-term b…
One-shot backpropagation for multi-step prediction in physics-based system identification -- EXTENDED VERSION
Cesare Donati, Martina Mammarella, Fabrizio Dabbene +2
The aim of this paper is to present a novel physics-based framework for the identification of dynamical systems, in which the physical and structural insights are reflected directl…