Nicola Demo

788 total citations
22 papers, 336 citations indexed

About

Nicola Demo is a scholar working on Statistical and Nonlinear Physics, Statistics, Probability and Uncertainty and Computational Mechanics. According to data from OpenAlex, Nicola Demo has authored 22 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Statistical and Nonlinear Physics, 8 papers in Statistics, Probability and Uncertainty and 6 papers in Computational Mechanics. Recurrent topics in Nicola Demo's work include Model Reduction and Neural Networks (16 papers), Probabilistic and Robust Engineering Design (8 papers) and Nuclear Engineering Thermal-Hydraulics (4 papers). Nicola Demo is often cited by papers focused on Model Reduction and Neural Networks (16 papers), Probabilistic and Robust Engineering Design (8 papers) and Nuclear Engineering Thermal-Hydraulics (4 papers). Nicola Demo collaborates with scholars based in Italy, United States and Netherlands. Nicola Demo's co-authors include Gianluigi Rozza, Marco Tezzele, Giovanni Stabile, Andrea Mola, Michele Girfoglio, Davide Fransos, Federico Toschi and Dimitri Breda and has published in prestigious journals such as Scientific Reports, Computer Methods in Applied Mechanics and Engineering and International Journal for Numerical Methods in Engineering.

In The Last Decade

Nicola Demo

22 papers receiving 326 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nicola Demo Italy 11 207 118 80 71 37 22 336
Marco Tezzele Italy 11 166 0.8× 97 0.8× 77 1.0× 54 0.8× 44 1.2× 20 353
Elizabeth Qian United States 7 215 1.0× 108 0.9× 98 1.2× 96 1.4× 42 1.1× 10 378
Alessandro Alla Italy 9 331 1.6× 156 1.3× 107 1.3× 39 0.5× 57 1.5× 27 469
George Em Karniadakis United States 6 219 1.1× 126 1.1× 53 0.7× 47 0.7× 63 1.7× 14 369
Yiping Lu China 5 260 1.3× 93 0.8× 67 0.8× 38 0.5× 101 2.7× 17 386
Saleh Nabi United States 9 128 0.6× 80 0.7× 36 0.5× 50 0.7× 37 1.0× 27 323
Andrea Mola Italy 12 260 1.3× 287 2.4× 97 1.2× 86 1.2× 20 0.5× 36 471
Levi D. McClenny United States 6 195 0.9× 68 0.6× 27 0.3× 60 0.8× 58 1.6× 7 342
Jian Cheng Wong Singapore 9 230 1.1× 105 0.9× 24 0.3× 62 0.9× 50 1.4× 22 430

Countries citing papers authored by Nicola Demo

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Nicola Demo

This figure shows the co-authorship network connecting the top 25 collaborators of Nicola Demo. A scholar is included among the top collaborators of Nicola Demo 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 Nicola Demo. Nicola Demo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Demo, Nicola, et al.. (2025). Kinetic data-driven approach to turbulence subgrid modeling. Physical Review Research. 7(1). 1 indexed citations
2.
Demo, Nicola, et al.. (2024). Data-driven discovery of delay differential equations with discrete delays. Journal of Computational and Applied Mathematics. 461. 116439–116439. 1 indexed citations
3.
Demo, Nicola, et al.. (2024). Large-scale graph-machine-learning surrogate models for 3D-flowfield prediction in external aerodynamics. Advanced Modeling and Simulation in Engineering Sciences. 11(1). 7 indexed citations
4.
Demo, Nicola, et al.. (2024). A shape optimization pipeline for marine propellers by means of reduced order modeling techniques. International Journal for Numerical Methods in Engineering. 125(7). 2 indexed citations
5.
Demo, Nicola, et al.. (2024). Generative adversarial reduced order modelling. Scientific Reports. 14(1). 3826–3826. 3 indexed citations
6.
Demo, Nicola, et al.. (2023). A dimensionality reduction approach for convolutional neural networks. Applied Intelligence. 53(19). 22818–22833. 5 indexed citations
7.
Demo, Nicola, et al.. (2023). A continuous convolutional trainable filter for modelling unstructured data. Computational Mechanics. 72(2). 253–265. 3 indexed citations
8.
9.
Demo, Nicola, et al.. (2023). An extended physics informed neural network for preliminary analysis of parametric optimal control problems. Computers & Mathematics with Applications. 143. 383–396. 16 indexed citations
10.
Demo, Nicola, Marco Tezzele, & Gianluigi Rozza. (2023). A DeepONet multi-fidelity approach for residual learning in reduced order modeling. Advanced Modeling and Simulation in Engineering Sciences. 10(1). 14 indexed citations
11.
Demo, Nicola, et al.. (2023). Physics-Informed Neural networks for Advancedmodeling. The Journal of Open Source Software. 8(87). 5352–5352. 8 indexed citations
12.
Demo, Nicola, et al.. (2023). A Dynamic Mode Decomposition Extension for the Forecasting of Parametric Dynamical Systems. SIAM Journal on Applied Dynamical Systems. 22(3). 2432–2458. 19 indexed citations
13.
Demo, Nicola, et al.. (2022). The Neural Network shifted-proper orthogonal decomposition: A machine learning approach for non-linear reduction of hyperbolic equations. Computer Methods in Applied Mechanics and Engineering. 392. 114687–114687. 36 indexed citations
14.
Demo, Nicola, et al.. (2022). A Proper Orthogonal Decomposition Approach for Parameters Reduction of Single Shot Detector Networks. 2022 IEEE International Conference on Image Processing (ICIP). 2206–2210. 1 indexed citations
15.
Demo, Nicola, et al.. (2021). A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics. Mathematics in Engineering. 4(3). 1–16. 27 indexed citations
16.
Demo, Nicola, Marco Tezzele, & Gianluigi Rozza. (2021). A Supervised Learning Approach Involving Active Subspaces for an Efficient Genetic Algorithm in High-Dimensional Optimization Problems. SIAM Journal on Scientific Computing. 43(3). B831–B853. 15 indexed citations
17.
Tezzele, Marco, Nicola Demo, Andrea Mola, & Gianluigi Rozza. (2020). PyGeM: Python Geometrical Morphing. Software Impacts. 7. 100047–100047. 22 indexed citations
18.
Demo, Nicola, Marco Tezzele, & Gianluigi Rozza. (2019). A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces. Comptes Rendus Mécanique. 347(11). 873–881. 30 indexed citations
19.
Demo, Nicola, Marco Tezzele, & Gianluigi Rozza. (2018). EZyRB: Easy Reduced Basis method. The Journal of Open Source Software. 3(24). 661–661. 27 indexed citations
20.
Demo, Nicola, Marco Tezzele, & Gianluigi Rozza. (2018). PyDMD: Python Dynamic Mode Decomposition. The Journal of Open Source Software. 3(22). 530–530. 71 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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