MENU

You are here

Publications

Export 23 results:
Filters: Author is Nicola Demo  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
A
Rozza G, Malik MH, Demo N, Tezzele M, Girfoglio M, Stabile G, Mola A. Advances in reduced order methods for parametric industrial problems in computational fluid dynamics. In: Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018. Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018. ; 2020. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075395686&partnerID=40&md5=fb0b1a3cfdfd35a104db9921bc9be675
C
Demo N, Tezzele M, Mola A, Rozza G. A complete data-driven framework for the efficient solution of parametric shape design and optimisation in naval engineering problems. In: VIII International Conference on Computational Methods in Marine Engineering. VIII International Conference on Computational Methods in Marine Engineering. ; 2019. Available from: https://arxiv.org/abs/1905.05982
Demo N, Tezzele M, Mola A, Rozza G. A complete data-driven framework for the efficient solution of parametric shape design and optimisation in naval engineering problems. In: 8th International Conference on Computational Methods in Marine Engineering, MARINE 2019. 8th International Conference on Computational Methods in Marine Engineering, MARINE 2019. ; 2019. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075342565&partnerID=40&md5=d76b8a1290053e7a84fb8801c0e6bb3d
E
Demo N, Ortali G, Gustin G, Rozza G, Lavini G. An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques. Bolletino dell Unione Matematica Italiana. 2021 ;14:211-230.
Demo N, Tezzele M, Mola A, Rozza G. An efficient shape parametrisation by free-form deformation enhanced by active subspace for hull hydrodynamic ship design problems in open source environment. The 28th International Ocean and Polar Engineering Conference [Internet]. 2018 . Available from: https://www.onepetro.org/conference-paper/ISOPE-I-18-481
Tezzele M, Demo N, Stabile G, Mola A, Rozza G. Enhancing CFD predictions in shape design problems by model and parameter space reduction. Advanced Modeling and Simulation in Engineering Sciences [Internet]. 2020 ;7(40). Available from: https://arxiv.org/abs/2001.05237
Calore E, Demo N, Schifano SFabio, Tripiccione R. Experience on vectorizing lattice Boltzmann kernels for multi-and many-core architectures. In: International Conference on Parallel Processing and Applied Mathematics. International Conference on Parallel Processing and Applied Mathematics. Springer; 2015. pp. 53–62.
Demo N, Strazzullo M, Rozza G. AN EXTENDED PHYSICS INFORMED NEURAL NETWORK FOR PRELIMINARY ANALYSIS OF PARAMETRIC OPTIMAL CONTROL PROBLEMS. 2021 .
Demo N, Tezzele M, Rozza G. EZyRB: Easy Reduced Basis method. The Journal of Open Source Software [Internet]. 2018 ;3:661. Available from: https://joss.theoj.org/papers/10.21105/joss.00661
M
Tezzele M, Demo N, Gadalla M, Mola A, Rozza G. Model Order Reduction by means of Active Subspaces and Dynamic Mode Decomposition for Parametric Hull Shape Design Hydrodynamics. In: Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship & Maritime Research. Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship & Maritime Research. Trieste, Italy: IOS Press; 2018. Available from: http://ebooks.iospress.nl/publication/49270
S
Demo N, Tezzele M, Gustin G, Lavini G, Rozza G. Shape Optimization by means of Proper Orthogonal Decomposition and Dynamic Mode Decomposition. In: Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship & Maritime Research. Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship & Maritime Research. Trieste, Italy: IOS Press; 2018. Available from: http://ebooks.iospress.nl/publication/49229
Tezzele M, Demo N, Rozza G. Shape optimization through proper orthogonal decomposition with interpolation and dynamic mode decomposition enhanced by active subspaces. In: 8th International Conference on Computational Methods in Marine Engineering, MARINE 2019. 8th International Conference on Computational Methods in Marine Engineering, MARINE 2019. ; 2019. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075390244&partnerID=40&md5=3e1f2e9a2539d34594caff13766c94b8
Demo N, Tezzele M, Rozza G. A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems. SIAM Journal on Scientific Computing [Internet]. 2021 ;43(3). Available from: https://arxiv.org/abs/2006.07282

Sign in