Matteo Diez
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- Probabilistic and Robust Engineering Design 43
- Ocean Engineering top 0.5%
- Ship Hydrodynamics and Maneuverability 59
- Computational Theory and Mathematics top 0.5%
- Advanced Multi-Objective Optimization Algorithms 43
- Environmental Engineering top 5%
- Maritime Transport Emissions and Efficiency 11
- Computational Mechanics top 2%
- Fluid Dynamics Simulations and Interactions 12
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- Topology Optimization in Engineering 14
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- Model Reduction and Neural Networks 10
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- Metaheuristic Optimization Algorithms Research 10
Matteo Diez
112 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 69
- Statistics, Probability and Uncertainty 471
- Ocean Engineering 832
- Computational Theory and Mathematics 621
- Environmental Engineering 316
- Computational Mechanics 437
Countries citing papers authored by Matteo Diez
This map shows the geographic impact of Matteo Diez'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 Matteo Diez with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matteo Diez more than expected).
Fields of papers citing papers by Matteo Diez
This network shows the impact of papers produced by Matteo Diez. 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 Matteo Diez. The network helps show where Matteo Diez may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Matteo Diez, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | 2025 | 0 | |
| 6 | 2025 | 0 | |
| 7 | 2025 | 2 | |
| 8 | 2024 | 3 | |
| 9 | 2024 | 4 | |
| 10 | 2024 | 4 | |
| 11 | 2024 | 0 | |
| 12 | 2023 | 1 | |
| 13 | 2023 | 4 | |
| 14 | 2023 | 2 | |
| 15 | 2022 | 1 | |
| 16 | Multi-Fidelity Machine Learning from Adaptive-and Multi-Grid RANS Simulations | 2020 | 4 |
| 17 | Validation of high fidelity CFD/FE FSI for full-scale high-speed planing hull with composite bottom panels slamming | 2015 | 4 |
| 18 | Resistance reduction of a military ship by variable-accuracy metamodel-based multidisciplinary robust design optimization | 2015 | 3 |
| 19 | CFD-Based Multiobjective Stochastic Optimization of a Waterjet Propelled High Speed Ship(Summaries of Papers published by Staff of National Maritime Research Institute at Outside Organizations) | 2012 | 0 |
| 20 | 2010 | 4 |
About Matteo Diez
Matteo Diez is a scholar working on Statistics, Probability and Uncertainty, Ocean Engineering and Computational Theory and Mathematics, having authored 125 papers that have together received 1.7k indexed citations. Recurring topics across this work include Ship Hydrodynamics and Maneuverability (59 papers), Advanced Multi-Objective Optimization Algorithms (43 papers), Probabilistic and Robust Engineering Design (43 papers), Topology Optimization in Engineering (14 papers), Fluid Dynamics Simulations and Interactions (12 papers), Maritime Transport Emissions and Efficiency (11 papers), Model Reduction and Neural Networks (10 papers) and Metaheuristic Optimization Algorithms Research (10 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (471 citations), Ocean Engineering (832 citations) and Computational Theory and Mathematics (621 citations). Matteo Diez has collaborated with scholars based in Italy, United States and France. Frequent co-authors include Emilio F. Campana, Frederick Stern, Andrea Serani, Umberto Iemma, Daniële Peri, Giovanni Fasano, Giampaolo Liuzzi, Stefano Lucidi, Riccardo Broglia and L. Morino.
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.