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Filters: Author is Gianluigi Rozza  [Clear All Filters]
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Hess MW, Quaini A, Rozza G. Data-Driven Enhanced Model Reduction for Bifurcating Models in Computational Fluid Dynamics. 2022 .
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 .
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
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 .
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.
Meneghetti L, Demo N, Rozza G. A Dimensionality Reduction Approach for Convolutional Neural Networks. 2021 .
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.
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 .
Andreuzzi F, Demo N, Rozza G. A dynamic mode decomposition extension for the forecasting of parametric dynamical systems. arXiv preprint arXiv:2110.09155. 2021 .
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Pintore M, Pichi F, Hess MW, Rozza G, Canuto C. Efficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method. Advances in Computational Mathematics [Internet]. 2020 . Available from: https://arxiv.org/abs/1912.06089
Pintore M, Pichi F, Hess MW, Rozza G, Canuto C. Efficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method. Advances in Computational Mathematics. 2021 ;47.
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.
Forti D, Rozza G. Efficient geometrical parametrisation techniques of interfaces for reduced-order modelling: application to fluid–structure interaction coupling problems. International Journal of Computational Fluid Dynamics. 2014 ;28:158–169.
Stabile G, Zancanaro M, Rozza G. Efficient Geometrical parametrization for finite-volume based reduced order methods. International Journal for Numerical Methods in Engineering [Internet]. 2020 ;121:2655-2682. Available from: https://arxiv.org/abs/1901.06373
Mola A, Tezzele M, Gadalla M, Valdenazzi F, Grassi D, Padovan R, Rozza G. Efficient reduction in shape parameter space dimension for ship propeller blade design. 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-s2.0-85075395143&partnerID=40&md5=b6aa0fcedc2f88e78c295d0f437824d0
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
Hijazi S, Ali S, Stabile G, Ballarin F, Rozza G. The Effort of Increasing Reynolds Number in Projection-Based Reduced Order Methods: from Laminar to Turbulent Flows. In: Lecture Notes in Computational Science and Engineering. Lecture Notes in Computational Science and Engineering. Cham: Springer International Publishing; 2020. pp. 245–264.
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
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
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Ballarin F, Faggiano E, Ippolito S, Manzoni A, Quarteroni A, Rozza G, Scrofani R. Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a POD-Galerkin method and a vascular shape parametrization.; 2015. Available from: http://urania.sissa.it/xmlui/handle/1963/34623
Ballarin F, Faggiano E, Manzoni A, Rozza G, Quarteroni A, Ippolito S, Scrofani R, Antona C. A fast virtual surgery platform for many scenarios haemodynamics of patient-specific coronary artery bypass grafts. Submitted; 2016. Available from: http://urania.sissa.it/xmlui/handle/1963/35240
Girfoglio M, Quaini A, Rozza G. A Finite Volume approximation of the Navier-Stokes equations with nonlinear filtering stabilization. Computers & Fluids [Internet]. 2019 ;187:27-45. Available from: https://arxiv.org/abs/1901.05251
Girfoglio M, Quaini A, Rozza G. A Finite Volume approximation of the Navier-Stokes equations with nonlinear filtering stabilization. Computers and Fluids [Internet]. 2019 ;187:27-45. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065471890&doi=10.1016%2fj.compfluid.2019.05.001&partnerID=40&md5=c982371b5b5d4b5664a676902aaa60f4
Stabile G, Rozza G. Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equations. Computers and Fluids [Internet]. 2018 ;173:273-284. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043366603&doi=10.1016%2fj.compfluid.2018.01.035&partnerID=40&md5=c15435ea3b632e55450da19ba2bb6125

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