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2020 – today
- 2024
[c10]Lea Multerer, Stefano Toniolo, Sandra Mitrovic, Maria Concetta Palumbo, Alessandro Ravoni, Paolo Tieri, Marco Forgione, Laura Azzimonti:
A Computationally Efficient Deep Learning-Based Surrogate Model of Prediabetes Progression. BIBM 2024: 6951-6958- [c9]Manuel Bianchi Bazzi, Asad Ali Shahid, Christopher Agia, John Irvin Alora, Marco Forgione, Dario Piga, Francesco Braghin, Marco Pavone, Loris Roveda:
RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling. ICINCO (2) 2024: 149-156 - [i17]Dario Piga
, Matteo Rufolo, Gabriele Maroni, Manas Mejari, Marco Forgione:
Synthetic data generation for system identification: leveraging knowledge transfer from similar systems. CoRR abs/2403.05164 (2024) - [i16]Marco Forgione, Manas Mejari, Dario Piga:
Model order reduction of deep structured state-space models: A system-theoretic approach. CoRR abs/2403.14833 (2024) - [i15]Manuel Bianchi Bazzi, Asad Ali Shahid, Christopher Agia, John Irvin Alora, Marco Forgione, Dario Piga, Francesco Braghin, Marco Pavone, Loris Roveda:
RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling. CoRR abs/2409.11815 (2024) - [i14]Matteo Rufolo, Dario Piga, Gabriele Maroni, Marco Forgione:
Enhanced Transformer architecture for in-context learning of dynamical systems. CoRR abs/2410.03291 (2024) - [i13]Angelo Moroncelli, Vishal Soni, Asad Ali Shahid, Marco Maccarini, Marco Forgione, Dario Piga, Blerina Spahiu, Loris Roveda:
Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and Perspectives. CoRR abs/2410.16411 (2024) - [i12]Riccardo Busetto, Valentina Breschi, Marco Forgione, Dario Piga, Simone Formentin:
One controller to rule them all. CoRR abs/2411.06482 (2024) - 2023
- [j9]Marco Forgione, Aneri Muni, Dario Piga, Marco Gallieri:
On the adaptation of recurrent neural networks for system identification. Autom. 155: 111092 (2023) - [j8]Marco Forgione, Filippo Pura, Dario Piga:
From System Models to Class Models: An In-Context Learning Paradigm. IEEE Control. Syst. Lett. 7: 3513-3518 (2023) - [i11]Marco Forgione, Dario Piga:
Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification. CoRR abs/2304.06349 (2023) - [i10]Marco Forgione, Filippo Pura, Dario Piga:
In-context learning for model-free system identification. CoRR abs/2308.13380 (2023) - [i9]Dario Piga, Filippo Pura, Marco Forgione:
On the adaptation of in-context learners for system identification. CoRR abs/2312.04083 (2023) - [i8]Riccardo Busetto, Valentina Breschi, Marco Forgione, Dario Piga, Simone Formentin:
In-context learning of state estimators. CoRR abs/2312.04509 (2023) - 2022
- [j7]Manas Mejari, Bojan Mavkov, Marco Forgione, Dario Piga:
Direct identification of continuous-time LPV state-space models via an integral architecture. Autom. 142: 110407 (2022) - [j6]Dario Piga, Manas Mejari, Marco Forgione:
Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective. IEEE Trans. Autom. Control. 67(10): 5471-5478 (2022) - [c8]Xavier Bombois, Marco Forgione:
Control Design via Bayesian Optimization with Safety Constraints. CCTA 2022: 1421-1426 - [i7]Marco Forgione, Aneri Muni, Dario Piga, Marco Gallieri:
On the adaptation of recurrent neural networks for system identification. CoRR abs/2201.08660 (2022) - [i6]Marco Forgione, Manas Mejari, Dario Piga:
Learning neural state-space models: do we need a state estimator? CoRR abs/2206.12928 (2022) - 2021
- [j5]Marco Forgione, Dario Piga:
Continuous-time system identification with neural networks: Model structures and fitting criteria. Eur. J. Control 59: 69-81 (2021) - [i5]Dario Piga, Marco Forgione, Manas Mejari:
Deep learning with transfer functions: new applications in system identification. CoRR abs/2104.09839 (2021) - 2020
- [j4]Bojan Mavkov, Marco Forgione, Dario Piga:
Integrated Neural Networks for Nonlinear Continuous-Time System Identification. IEEE Control. Syst. Lett. 4(4): 851-856 (2020) - [c7]Loris Roveda, Marco Forgione, Dario Piga:
One-Stage Auto-Tuning Procedure of Robot Dynamics and Control Parameters for Trajectory Tracking Applications. UR 2020: 105-112 - [i4]Marco Forgione, Dario Piga:
dynoNet: a neural network architecture for learning dynamical systems. CoRR abs/2006.02250 (2020) - [i3]Marco Forgione, Dario Piga:
Continuous-time system identification with neural networks: model structures and fitting criteria. CoRR abs/2006.02915 (2020)
2010 – 2019
- 2019
- [j3]Dario Piga, Marco Forgione, Simone Formentin, Alberto Bemporad:
Performance-Oriented Model Learning for Data-Driven MPC Design. IEEE Control. Syst. Lett. 3(3): 577-582 (2019) - [i2]Marco Forgione, Dario Piga, Alberto Bemporad:
Efficient Calibration of Embedded MPC. CoRR abs/1911.13021 (2019) - [i1]Marco Forgione, Dario Piga:
Model structures and fitting criteria for system identification with neural networks. CoRR abs/1911.13034 (2019) - 2015
- [j2]Marco Forgione, Xavier Bombois, Paul M. J. Van den Hof:
Data-driven model improvement for model-based control. Autom. 52: 118-124 (2015) - [j1]Ali Mesbah, Xavier Bombois, Marco Forgione, Håkan Hjalmarsson, Paul M. J. Van den Hof:
Least costly closed-loop performance diagnosis and plant re-identification. Int. J. Control 88(11): 2264-2276 (2015) - [c6]Max G. Potters, Marco Forgione, Xavier Bombois, Paul M. J. Van den Hof:
Least-costly experiment design for uni-parametric linear models: An analytical approach. ECC 2015: 848-853 - 2014
- [c5]Marco Forgione, Xavier Bombois, Paul M. J. Van den Hof, Håkan Hjalmarsson:
Experiment design for parameter estimation in nonlinear systems based on multilevel excitation. ECC 2014: 25-30 - 2013
- [c4]Marco Forgione, Xavier Bombois, Paul M. J. Van den Hof:
Experiment design for batch-to-batch model-based learning control. ACC 2013: 3912-3917 - 2012
- [c3]Marco Forgione, Ali Mesbah, Xavier Bombois, Paul M. J. Van den Hof:
Iterative Learning Control of supersaturation in batch cooling crystallization. ACC 2012: 6455-6460 - [c2]Ali Mesbah, Xavier Bombois, Marco Forgione, Jobert H. A. Ludlage, Per Erik Modén, Håkan Hjalmarsson, Paul M. J. Van den Hof:
A unified experiment design framework for detection and identification in closed-loop performance diagnosis. CDC 2012: 2152-2157 - [c1]Marco Forgione, Ali Mesbah, Xavier Bombois, Paul M. J. Van den Hof:
Batch-to-batch strategies for cooling crystallization. CDC 2012: 6364-6369
Coauthor Index
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