| PyDMD: Python dynamic mode decomposition N Demo, M Tezzele, G Rozza Journal of Open Source Software 3 (22), 530, 2018 | 107 | 2018 |
| Advances in reduced order methods for parametric industrial problems in computational fluid dynamics G Rozza, MH Malik, N Demo, M Tezzele, M Girfoglio, G Stabile, A Mola | 51* | |
| Shape optimization by means of proper orthogonal decomposition and dynamic mode decomposition A Marinò, V Bucci Technology and Science for the Ships of the Future: Proceedings of NAV 2018 …, 2018 | 48 | 2018 |
| A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces N Demo, M Tezzele, G Rozza Comptes Rendus. Mécanique 347 (11), 873-881, 2019 | 46 | 2019 |
| An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics M Tezzele, N Demo, A Mola, G Rozza Novel Mathematics Inspired by Industrial Challenges, 179-200, 2022 | 43 | 2022 |
| The neural network shifted-proper orthogonal decomposition: a machine learning approach for non-linear reduction of hyperbolic equations D Papapicco, N Demo, M Girfoglio, G Stabile, G Rozza Computer Methods in Applied Mechanics and Engineering 392, 114687, 2022 | 43 | 2022 |
| EZyRB: Easy Reduced Basis method N Demo, M Tezzele, G Rozza The Journal of Open Source Software 3 (24), 661, 2018 | 42 | 2018 |
| Enhancing CFD predictions in shape design problems by model and parameter space reduction M Tezzele, N Demo, G Stabile, A Mola, G Rozza Advanced Modeling and Simulation in Engineering Sciences 7, 1-19, 2020 | 35 | 2020 |
| Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing N Demo, M Tezzele, A Mola, G Rozza Journal of Marine Science and Engineering 9 (2), 185, 2021 | 31 | 2021 |
| Shape optimization through proper orthogonal decomposition with interpolation and dynamic mode decomposition enhanced by active subspaces M Tezzele, N Demo, G Rozza arXiv preprint arXiv:1905.05483, 2019 | 30 | 2019 |
| Model order reduction by means of active subspaces and dynamic mode decomposition for parametric hull shape design hydrodynamics M Tezzele, N Demo, M Gadalla, A Mola, G Rozza arXiv preprint arXiv:1803.07377, 2018 | 30 | 2018 |
| An extended physics informed neural network for preliminary analysis of parametric optimal control problems N Demo, M Strazzullo, G Rozza Computers & Mathematics with Applications 143, 383-396, 2023 | 28 | 2023 |
| An efficient shape parametrisation by free-form deformation enhanced by active subspace for hull hydrodynamic ship design problems in open source environment N Demo, M Tezzele, A Mola, G Rozza ISOPE International Ocean and Polar Engineering Conference, ISOPE-I-18-481, 2018 | 28 | 2018 |
| An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques N Demo, G Ortali, G Gustin, G Rozza, G Lavini Bollettino dell'Unione Matematica Italiana 14, 211-230, 2021 | 27 | 2021 |
| PyGeM: Python geometrical morphing M Tezzele, N Demo, A Mola, G Rozza Software impacts 7, 100047, 2021 | 27 | 2021 |
| A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems N Demo, M Tezzele, G Rozza SIAM Journal on Scientific Computing 43 (3), B831-B853, 2021 | 27 | 2021 |
| A Gaussian process regression approach within a data-driven POD framework for engineering problems in fluid dynamics G Ortali, N Demo, G Rozza Mathematics in Engineering 4 (3), 1-16, 2022 | 26* | 2022 |
| A complete data-driven framework for the efficient solution of parametric shape design and optimisation in naval engineering problems N Demo, M Tezzele, A Mola, G Rozza arXiv preprint arXiv:1905.05982, 2019 | 24 | 2019 |
| Reduced order isogeometric analysis approach for PDEs in parametrized domains F Garotta, N Demo, M Tezzele, M Carraturo, A Reali, G Rozza Quantification of Uncertainty: Improving Efficiency and Technology, 153-170, 2020 | 23 | 2020 |
| Experience on vectorizing lattice Boltzmann kernels for multi-and many-core architectures E Calore, N Demo, SF Schifano, R Tripiccione Parallel Processing and Applied Mathematics: 11th International Conference …, 2016 | 17 | 2016 |
Gianluigi RozzaFull Professor of Numerical Analysis, SISSA, Int. School for Adv. Studies, Italy and ERC PIVerified email at sissa.it
