Optimal design of clinical tests for the identification of physiological models of type 1 diabetes in the presence of model mismatch. - Federico Galvanin; Massimiliano Barolo; Sandro Macchietto; Fabrizio Bezzo (Medical Biol. Eng. Comput., 2011)

Online model-based redesign of experiments with erratic models: A disturbance estimation approach. - Federico Galvanin; Massimiliano Barolo; Gabriele Pannocchia; Fabrizio Bezzo (Comput. Chem. Eng., 2012)

On the use of continuous glucose monitoring systems to design optimal clinical tests for the identification of type 1 diabetes models. - Federico Galvanin; Massimiliano Barolo; Fabrizio Bezzo (Comput. Methods Programs Biomed., 2013)

A joint model-based experimental design approach for the identification of kinetic models in continuous flow laboratory reactors. - Federico Galvanin; Enhong Cao; Noor Al-Rifai; Asterios Gavriilidis; Vivek Dua (Comput. Chem. Eng., 2016)

A methodology for direct exploitation of available information in the online model-based redesign of experiments. - Riccardo De Luca; Federico Galvanin; Fabrizio Bezzo (Comput. Chem. Eng., 2016)

An online reparametrisation approach for robust parameter estimation in automated model identification platforms. - Marco Quaglio; Conor Waldron; Arun Pankajakshan; Enhong Cao; Asterios Gavriilidis; Eric S. Fraga; Federico Galvanin (Comput. Chem. Eng., 2019)

A framework for the optimal design of a minimum set of clinical trials to characterize von Willebrand disease. - B. Taverna; A. Casonato; Fabrizio Bezzo; Federico Galvanin (Comput. Methods Programs Biomed., 2019)

A diagnostic procedure for improving the structure of approximated kinetic models. - Marco Quaglio; Eric S. Fraga; Federico Galvanin (Comput. Chem. Eng., 2020)

An artificial neural network approach to recognise kinetic models from experimental data. - Marco Quaglio; Louise Roberts; Mohd Safarizal Bin Jaapar; Eric S. Fraga; Vivek Dua; Federico Galvanin (Comput. Chem. Eng., 2020)

Chance Constrained Policy Optimization for Process Control and Optimization. - Panagiotis Petsagkourakis; Ilya Orson Sandoval; Eric Bradford; Federico Galvanin; Dongda Zhang; Ehecatl Antonio del Rio-Chanona (CoRR, 2020)

Safe model-based design of experiments using Gaussian processes. - Panagiotis Petsagkourakis; Federico Galvanin (Comput. Chem. Eng., 2021)

An optimal experimental design strategy for improving parameter estimation in stochastic models. - Chunbing Huang; Federica Cattani; Federico Galvanin (Comput. Chem. Eng., 2023)

An exploratory model-based design of experiments approach to aid parameters identification and reduce model prediction uncertainty. - Francesca Cenci; Arun Pankajakshan; Pierantonio Facco; Federico Galvanin (Comput. Chem. Eng., 2023)

An optimization-free Fisher information driven approach for online design of experiments. - Andrea Friso; Federico Galvanin (Comput. Chem. Eng., 2024)

An optimal experimental design framework for fast kinetic model identification based on artificial neural networks. - Enrico Sangoi; Marco Quaglio; Fabrizio Bezzo; Federico Galvanin (Comput. Chem. Eng., 2024)

Model-based design of experiments for efficient and accurate isotherm model identification in High Performance Liquid Chromatography. - Konstantinos Katsoulas; Federico Galvanin; Luca Mazzei; Maximilian O. Besenhard; Eva Sørensen (Comput. Chem. Eng., 2025)

DoE-SINDy: an automated framework for model generation and selection in kinetic studies. - Wenyao Lyu; Federico Galvanin (Comput. Chem. Eng., 2025)