Nicola Demo
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- Model Reduction and Neural Networks 16
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- Probabilistic and Robust Engineering Design 8
- Computational Mechanics top 10%
- Fluid Dynamics and Turbulent Flows 3
- Fluid Dynamics and Vibration Analysis 3
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- Nuclear Engineering Thermal-Hydraulics 4
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- Structural Health Monitoring Techniques 2
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- Ship Hydrodynamics and Maneuverability 2
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- Computational Physics and Python Applications 2
- Co-authors
- Gianluigi RozzaMarco TezzeleGiovanni StabileAndrea MolaMichele GirfoglioDavide FransosDimitri BredaFederico Toschi
- Cited by
- Statistical and Nonlinear PhysicsStatistics, Probability and UncertaintyComputational Mechanics
- Journals
- Scientific Reports (1 paper)Computer Methods in Applied Mechanics and Engineering (1 paper)International Journal for Numerical Methods in Engineering (1 paper)
- Partner nations
- ItalyUnited StatesNetherlands
In The Last Decade
Nicola Demo
22 papers receiving 326 citations
Peers
Comparison fields: 5 of 68
- Statistical and Nonlinear Physics 207
- Statistics, Probability and Uncertainty 80
- Computational Mechanics 118
- Computational Mathematics 2
- Aerospace Engineering 71
Countries citing papers authored by Nicola Demo
This map shows the geographic impact of Nicola Demo'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 Nicola Demo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicola Demo more than expected).
Fields of papers citing papers by Nicola Demo
This network shows the impact of papers produced by Nicola Demo. 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 Nicola Demo. The network helps show where Nicola Demo may publish in the future.
Co-authorship network
The 8 scholars most cited alongside Nicola Demo, 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 | 1 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 3 | |
| 5 | 2024 | 7 | |
| 6 | 2023 | 5 | |
| 7 | 2023 | 5 | |
| 8 | 2023 | 14 | |
| 9 | 2023 | 8 | |
| 10 | 2023 | 3 | |
| 11 | 2023 | 16 | |
| 12 | 2023 | 19 | |
| 13 | 2022 | 36 | |
| 14 | 2022 | 1 | |
| 15 | 2021 | 27 | |
| 16 | 2021 | 15 | |
| 17 | 2020 | 22 | |
| 18 | 2019 | 30 | |
| 19 | 2018 | 27 | |
| 20 | 2018 | 71 |
About Nicola Demo
Nicola Demo is a scholar working on Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty and Computational Mechanics, having authored 22 papers that have together received 336 indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (16 papers), Probabilistic and Robust Engineering Design (8 papers), Nuclear Engineering Thermal-Hydraulics (4 papers), Fluid Dynamics and Turbulent Flows (3 papers), Fluid Dynamics and Vibration Analysis (3 papers), Structural Health Monitoring Techniques (2 papers), Ship Hydrodynamics and Maneuverability (2 papers) and Computational Physics and Python Applications (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (207 citations), Statistics, Probability and Uncertainty (80 citations) and Computational Mechanics (118 citations). Nicola Demo has collaborated with scholars based in Italy, United States and Netherlands. Frequent co-authors include Gianluigi Rozza, Marco Tezzele, Giovanni Stabile, Andrea Mola, Michele Girfoglio, Davide Fransos, Dimitri Breda and Federico Toschi. Their work appears in journals such as Scientific Reports, Computer Methods in Applied Mechanics and Engineering and International Journal for Numerical Methods in Engineering.
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.