Iterative Learning Control of supersaturation in batch cooling crystallization. - Marco Forgione; Ali Mesbah 002; Xavier Bombois; Paul M. J. Van den Hof (ACC, 2012)

Batch-to-batch strategies for cooling crystallization. - Marco Forgione; Ali Mesbah 002; Xavier Bombois; Paul M. J. Van den Hof (CDC, 2012)

A unified experiment design framework for detection and identification in closed-loop performance diagnosis. - Ali Mesbah 002; Xavier Bombois; Marco Forgione; Jobert H. A. Ludlage; Per Erik Modén; Håkan Hjalmarsson; Paul M. J. Van den Hof (CDC, 2012)

Experiment design for batch-to-batch model-based learning control. - Marco Forgione; Xavier Bombois; Paul M. J. Van den Hof (ACC, 2013)

Experiment design for parameter estimation in nonlinear systems based on multilevel excitation. - Marco Forgione; Xavier Bombois; Paul M. J. Van den Hof; Håkan Hjalmarsson (ECC, 2014)

Least-costly experiment design for uni-parametric linear models: An analytical approach. - Max G. Potters; Marco Forgione; Xavier Bombois; Paul M. J. Van den Hof (ECC, 2015)

Data-driven model improvement for model-based control. - Marco Forgione; Xavier Bombois; Paul M. J. Van den Hof (Autom., 2015)

Least costly closed-loop performance diagnosis and plant re-identification. - Ali Mesbah 002; Xavier Bombois; Marco Forgione; Håkan Hjalmarsson; Paul M. J. Van den Hof (Int. J. Control, 2015)

Efficient Calibration of Embedded MPC. - Marco Forgione; Dario Piga; Alberto Bemporad (CoRR, 2019)

Model structures and fitting criteria for system identification with neural networks. - Marco Forgione; Dario Piga (CoRR, 2019)

Performance-Oriented Model Learning for Data-Driven MPC Design. - Dario Piga; Marco Forgione; Simone Formentin; Alberto Bemporad (IEEE Control. Syst. Lett., 2019)

One-Stage Auto-Tuning Procedure of Robot Dynamics and Control Parameters for Trajectory Tracking Applications. - Loris Roveda; Marco Forgione; Dario Piga (UR, 2020)

dynoNet: a neural network architecture for learning dynamical systems. - Marco Forgione; Dario Piga (CoRR, 2020)

Integrated Neural Networks for Nonlinear Continuous-Time System Identification. - Bojan Mavkov; Marco Forgione; Dario Piga (IEEE Control. Syst. Lett., 2020)

Deep learning with transfer functions: new applications in system identification. - Dario Piga; Marco Forgione; Manas Mejari (CoRR, 2021)

Continuous-time system identification with neural networks: Model structures and fitting criteria. - Marco Forgione; Dario Piga (Eur. J. Control, 2021)

Control Design via Bayesian Optimization with Safety Constraints. - Xavier Bombois; Marco Forgione (CCTA, 2022)

NARX Identification using Derivative-Based Regularized Neural Networks. - L. H. Peeters; Gerben Izaak Beintema; Marco Forgione; Maarten Schoukens (CDC, 2022)

Direct identification of continuous-time LPV state-space models via an integral architecture. - Manas Mejari; Bojan Mavkov; Marco Forgione; Dario Piga (Autom., 2022)

Learning neural state-space models: do we need a state estimator? - Marco Forgione; Manas Mejari; Dario Piga (CoRR, 2022)

Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective. - Dario Piga; Manas Mejari; Marco Forgione (IEEE Trans. Autom. Control., 2022)

Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification. - Marco Forgione; Dario Piga (CoRR, 2023)

In-context learning for model-free system identification. - Marco Forgione; Filippo Pura; Dario Piga (CoRR, 2023)

On the adaptation of in-context learners for system identification. - Dario Piga; Filippo Pura; Marco Forgione (CoRR, 2023)

In-context learning of state estimators. - Riccardo Busetto; Valentina Breschi; Marco Forgione; Dario Piga; Simone Formentin (CoRR, 2023)

From System Models to Class Models: An In-Context Learning Paradigm. - Marco Forgione; Filippo Pura; Dario Piga (IEEE Control. Syst. Lett., 2023)

On the adaptation of recurrent neural networks for system identification. - Marco Forgione; Aneri Muni; Dario Piga; Marco Gallieri (Autom., 2023)

A Computationally Efficient Deep Learning-Based Surrogate Model of Prediabetes Progression. - Lea Multerer; Stefano Toniolo; Sandra Mitrovic; Maria Concetta Palumbo; Alessandro Ravoni; Paolo Tieri; Marco Forgione; Laura Azzimonti (BIBM, 2024)

Model order reduction of deep structured state-space models: A system-theoretic approach. - Marco Forgione; Manas Mejari; Dario Piga (CDC, 2024)

Synthetic data generation for system identification: leveraging knowledge transfer from similar systems. - Dario Piga; Matteo Rufolo; Gabriele Maroni; Manas Mejari; Marco Forgione (CDC, 2024)

RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling. - Manuel Bianchi Bazzi; Asad Ali Shahid; Christopher Agia; John Irvin Alora; Marco Forgione; Dario Piga; Francesco Braghin; Marco Pavone 001; Loris Roveda (ICINCO (2), 2024)

Enhanced Transformer architecture for in-context learning of dynamical systems. - Matteo Rufolo; Dario Piga; Gabriele Maroni; Marco Forgione (CoRR, 2024)

Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and Perspectives. - Angelo Moroncelli; Vishal Soni; Asad Ali Shahid; Marco Maccarini; Marco Forgione; Dario Piga; Blerina Spahiu; Loris Roveda (CoRR, 2024)

One controller to rule them all. - Riccardo Busetto; Valentina Breschi; Marco Forgione; Dario Piga; Simone Formentin (CoRR, 2024)

Physics-Informed Neural Networks for Hidden Insulin Dynamics Estimation from Glucose Data. - Lea Multerer; Manuel Acquistapace; Marco Forgione; Laura Azzimonti (AIME (2), 2025)

Distributionally Robust Minimization in Meta-Learning for System Identification. - Matteo Rufolo; Dario Piga; Marco Forgione (IEEE Control. Syst. Lett., 2025)

dynoGP: Deep Gaussian Processes for dynamic system identification. - Alessio Benavoli; Dario Piga; Marco Forgione; Marco Zaffalon (CoRR, 2025)

In-Context Learning for Zero-Shot Speed Estimation of BLDC motors. - Alessandro Colombo; Riccardo Busetto; Valentina Breschi; Marco Forgione; Dario Piga; Simone Formentin (CoRR, 2025)

Manifold meta-learning for reduced-complexity neural system identification. - Marco Forgione; Ankush Chakrabarty; Dario Piga; Matteo Rufolo; Alberto Bemporad (CoRR, 2025)

Learning Low-Dimensional Embeddings for Black-Box Optimization. - Riccardo Busetto; Manas Mejari; Marco Forgione; Alberto Bemporad; Dario Piga (CoRR, 2025)