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Filters: Author is Gianluigi Rozza  [Clear All Filters]
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Andreuzzi F, Demo N, Rozza G. A dynamic mode decomposition extension for the forecasting of parametric dynamical systems. arXiv preprint arXiv:2110.09155. 2021 .
Pichi F, Strazzullo M, Ballarin F, Rozza G. Driving bifurcating parametrized nonlinear PDEs by optimal control strategies: application to Navier-Stokes equations with model order reduction. 2020 .
Shah N, Hess MW, Rozza G. Discontinuous Galerkin Model Order Reduction of Geometrically Parametrized Stokes Equation. In: Vermolen FJ, Vuik C Numerical Mathematics and Advanced Applications ENUMATH 2019. Numerical Mathematics and Advanced Applications ENUMATH 2019. Cham: Springer International Publishing; 2021.
Meneghetti L, Demo N, Rozza G. A Dimensionality Reduction Approach for Convolutional Neural Networks. 2021 .
Tezzele M, Salmoiraghi F, Mola A, Rozza G. Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems. Advanced Modeling and Simulation in Engineering Sciences. 2018 ;5:25.
Hess MW, Quaini A, Rozza G. A Data-Driven Surrogate Modeling Approach for Time-Dependent Incompressible Navier-Stokes Equations with Dynamic Mode Decomposition and Manifold Interpolation. 2022 .
Hijazi S, Stabile G, Mola A, Rozza G. Data-driven POD-Galerkin reduced order model for turbulent flows. Journal of Computational Physics [Internet]. 2020 ;416:109513. Available from: https://arxiv.org/abs/1907.09909
Donadini E, Strazzullo M, Tezzele M, Rozza G. A data-driven partitioned approach for the resolution of time-dependent optimal control problems with dynamic mode decomposition. 2021 .
Hess MW, Quaini A, Rozza G. Data-Driven Enhanced Model Reduction for Bifurcating Models in Computational Fluid Dynamics. 2022 .
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Strazzullo M, Girfoglio M, Ballarin F, Iliescu T, Rozza G. Consistency of the full and reduced order models for Evolve-Filter-Relax Regularization of Convection-Dominated, Marginally-Resolved Flows. 2021 .
Pitton G, Quaini A, Rozza G. Computational reduction strategies for the detection of steady bifurcations in incompressible fluid-dynamics: Applications to Coanda effect in cardiology. Journal of Computational Physics. 2017 ;344:557.
Auricchio F, Conti M, Lefieux A, Morganti S, Reali A, Rozza G, Veneziani A. Computational methods in cardiovascular mechanics. In: Labrosse MF Cardiovascular Mechanics. Cardiovascular Mechanics. CRC Press; 2018. p. 54. Available from: https://www.taylorfrancis.com/books/e/9781315280288/chapters/10.1201%2Fb21917-5
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
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
Hess MW, Quaini A, Rozza G. A comparison of reduced-order modeling approaches using artificial neural networks for PDEs with bifurcating solutions. ETNA - Electronic Transactions on Numerical Analysis. 2022 ;56:52–65.
Gadalla M, Cianferra M, Tezzele M, Stabile G, Mola A, Rozza G. On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis. Computers & Fluids [Internet]. 2021 ;216:104819. Available from: https://www.sciencedirect.com/science/article/pii/S0045793020303893
Sartori A, Baroli D, Cammi A, Chiesa D, Luzzi L, Ponciroli RR, Previtali E, Ricotti ME, Rozza G, Sisti M. Comparison of a Modal Method and a Proper Orthogonal Decomposition approach for multi-group time-dependent reactor spatial kinetics. Annals of Nuclear Energy [Internet]. 2014 ;71:229. Available from: http://urania.sissa.it/xmlui/handle/1963/35039
Chen P, Quarteroni A, Rozza G. Comparison between reduced basis and stochastic collocation methods for elliptic problems. [Internet]. 2014 . Available from: http://urania.sissa.it/xmlui/handle/1963/34727
Tezzele M, Ballarin F, Rozza G. Combined parameter and model reduction of cardiovascular problems by means of active subspaces and POD-Galerkin methods. In: Mathematical and Numerical Modeling of the Cardiovascular System and Applications. Mathematical and Numerical Modeling of the Cardiovascular System and Applications. Springer; 2018. pp. 185–207.
Devaud D, Manzoni A, Rozza G. A combination between the reduced basis method and the ANOVA expansion: On the computation of sensitivity indices. Comptes Rendus Mathematique. Volume 351, Issue 15-16, August 2013, Pages 593-598 [Internet]. 2013 . Available from: http://hdl.handle.net/1963/7389
Rebollo TC, Ávila ED, Marmol MG, Ballarin F, Rozza G. On a certified smagorinsky reduced basis turbulence model. SIAM Journal on Numerical Analysis [Internet]. 2017 ;55:3047-3067. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039928218&doi=10.1137%2f17M1118233&partnerID=40&md5=221d9cd2bcc74121fcef93efd9d3d76c
Ballarin F, Rebollo TC, Ávila ED, Marmol MG, Rozza G. Certified Reduced Basis VMS-Smagorinsky model for natural convection flow in a cavity with variable height. Computers and Mathematics with Applications [Internet]. 2020 ;80:973-989. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085843368&doi=10.1016%2fj.camwa.2020.05.013&partnerID=40&md5=7c6596865ec89651319c7dd97159dd77
Hesthaven JS, Rozza G, Stamm B. Certified Reduced Basis Methods for Parametrized Partial Differential Equations. 1st ed. Switzerland: Springer; 2015 p. 135.
Strazzullo M, Ballarin F, Rozza G. A CERTIFIED REDUCED BASIS Method FOR LINEAR PARAMETRIZED PARABOLIC OPTIMAL CONTROL PROBLEMS IN SPACE-TIME FORMULATION. 2021 .
Martini I, Haasdonk B, Rozza G. Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models. Journal of Scientific Computing [Internet]. 2018 ;74:197-219. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017156114&doi=10.1007%2fs10915-017-0430-y&partnerID=40&md5=023ef0bb95713f4442d1fa374c92a964

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