Mauro Gaggero

1.3k total citations
72 papers, 891 citations indexed

About

Mauro Gaggero is a scholar working on Control and Systems Engineering, Computational Mechanics and Industrial and Manufacturing Engineering. According to data from OpenAlex, Mauro Gaggero has authored 72 papers receiving a total of 891 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Control and Systems Engineering, 16 papers in Computational Mechanics and 13 papers in Industrial and Manufacturing Engineering. Recurrent topics in Mauro Gaggero's work include Advanced Control Systems Optimization (17 papers), Model Reduction and Neural Networks (9 papers) and Control Systems and Identification (8 papers). Mauro Gaggero is often cited by papers focused on Advanced Control Systems Optimization (17 papers), Model Reduction and Neural Networks (9 papers) and Control Systems and Identification (8 papers). Mauro Gaggero collaborates with scholars based in Italy, United States and Poland. Mauro Gaggero's co-authors include A. Alessandri, Luca Caviglione, Cristiano Cervellera, Flavio Tonelli, Patrizia Bagnerini, Danilo Macciò, Jean-François Lalande, Wojciech Mazurczyk, Marcello Sanguineti and Giorgio Gnecco and has published in prestigious journals such as IEEE Transactions on Automatic Control, Automatica and European Journal of Operational Research.

In The Last Decade

Mauro Gaggero

70 papers receiving 863 citations

Peers

Mauro Gaggero

CSE

CNC

IME

Aijia Ouyang China

Chongquan Zhong China

Arvind U. Raghunathan United States

Satyasai Jagannath Nanda India

Rachid Chelouah France

Salman A. Khan Saudi Arabia

Changsheng Zhang China

John W. Chinneck Canada

Aijia Ouyang China

Mauro Gaggero

157 ×0.6

CSE

242 ×1.2

CNC

424 ×2.5

65 ×0.5

IME

152 ×1.2

Citations per year, relative to Mauro Gaggero Mauro Gaggero (= 1×) peers Aijia Ouyang

Countries citing papers authored by Mauro Gaggero

Since Specialization

Citations

This map shows the geographic impact of Mauro Gaggero's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mauro Gaggero with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mauro Gaggero more than expected).

Fields of papers citing papers by Mauro Gaggero

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mauro Gaggero. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mauro Gaggero. The network helps show where Mauro Gaggero may publish in the future.

Co-authorship network of co-authors of Mauro Gaggero

This figure shows the co-authorship network connecting the top 25 collaborators of Mauro Gaggero. A scholar is included among the top collaborators of Mauro Gaggero based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mauro Gaggero. Mauro Gaggero is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Diez, Matteo, Mauro Gaggero, & Andrea Serani. (2024). Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition. International Journal of Adaptive Control and Signal Processing. 39(10). 2119–2142. 4 indexed citations

Bagnerini, Patrizia, et al.. (2024). Parameter Estimation of a Dynamic Growth Model for Lettuce in an Adaptive Vertical Farm. CINECA IRIS Institutial Research Information System (University of Genoa). 1169–1174.

Alessandri, A., Mauro Gaggero, & Marcello Sanguineti. (2023). Data‐driven performance metrics for neural network learning. International Journal of Adaptive Control and Signal Processing. 39(10). 2081–2092. 1 indexed citations

Bagnerini, Patrizia, et al.. (2023). The Adaptive Vertical Farm as an Efficient Tool for the Cultivation of Multiple Crops in Space. CINECA IRIS Institutial Research Information System (University of Genoa). 231–236. 5 indexed citations

Bagnerini, Patrizia, et al.. (2023). Mixed-Integer Linear Programming for the Scheduling of Seedings in an Industrial Adaptive Vertical Farm. CINECA IRIS Institutial Research Information System (University of Genoa). 1–6. 3 indexed citations

Cervellera, Cristiano, Mauro Gaggero, & Danilo Macciò. (2021). Policy Optimization for Berth Allocation Problems. 1–6. 3 indexed citations

Odetti, Angelo, Marco Bibuli, Gabriele Bruzzone, et al.. (2020). A preliminary experiment combining marine robotics and citizenship engagement using imitation learning. IFAC-PapersOnLine. 53(2). 14576–14581. 3 indexed citations

Cataliotti, Antonio, Cristiano Cervellera, Valentina Cosentino, et al.. (2018). An Improved Load Flow Method for MV Networks Based on LV Load Measurements and Estimations. IEEE Transactions on Instrumentation and Measurement. 68(2). 430–438. 23 indexed citations

Alessandri, A., Patrizia Bagnerini, Mauro Gaggero, & Anna Maria Rossi. (2018). Feedback Control on the Velocity Field and Source Term of a Normal Flow Equation. CNR ExploRA. 1714–1719. 1 indexed citations

10.

Alessandri, A., Patrizia Bagnerini, Mauro Gaggero, Davide Lengani, & Daniele Simoni. (2018). Moving Horizon Trend Identification Based on Switching Models for Data Driven Decomposition of Fluid Flows. CINECA IRIS Institutial Research Information System (University of Genoa). 2138–2143. 3 indexed citations

11.

Cervellera, Cristiano, Mauro Gaggero, & Danilo Macciò. (2017). Lattice point sets for state sampling in approximate dynamic programming. Optimal Control Applications and Methods. 38(6). 1193–1207. 5 indexed citations

12.

Paola, Donato Di, Mauro Gaggero, Antonio Petitti, & Luca Caviglione. (2016). Optimal control of time instants for task replanning in robotic networks. 24. 1993–1998. 4 indexed citations

13.

Cervellera, Cristiano, et al.. (2015). Efficient use of Nadaraya-Watson models and low-discrepancy sequences for approximate dynamic programming. 17. 1–8. 1 indexed citations

14.

Cervellera, Cristiano, et al.. (2014). Lattice sampling for efficient learning with Nadaraya-Watson local models. 124. 1915–1922. 3 indexed citations

15.

Cervellera, Cristiano, Mauro Gaggero, & Danilo Macciò. (2014). An analysis based on F-discrepancy for sampling in regression tree learning. 1115–1121. 4 indexed citations

16.

Cervellera, Cristiano, et al.. (2013). Quasi-random sampling for approximate dynamic programming. 1–8. 14 indexed citations

17.

Cervellera, Cristiano, Mauro Gaggero, & Danilo Macciò. (2013). Low-discrepancy sampling for approximate dynamic programming with local approximators. Computers & Operations Research. 43. 108–115. 17 indexed citations

18.

Alessandri, A., Marco Baglietto, Giorgio Battistelli, & Mauro Gaggero. (2011). Moving-Horizon State Estimation for Nonlinear Systems Using Neural Networks. IEEE Transactions on Neural Networks. 22(5). 768–780. 54 indexed citations

19.

Alessandri, A., Mauro Gaggero, & Flavio Tonelli. (2011). Integer tree-based search and mixed-integer optimal control of distribution chains. 489–494. 4 indexed citations

20.

Molinari, G., Piergiorgio Alotto, M. Nervi, & Mauro Gaggero. (1997). A "Design of Experiment" Approach to Enhance the "Generalized Response Surface" Method in the Optimization of Multiminima Problems. IEEE Transactions on Magnetics. 1896–1899. 9 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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