MENU

You are here

Publications

Export 155 results:
Filters: Author is Gianluigi Rozza  [Clear All Filters]
Journal Article
Demo N, Tezzele M, Rozza G. A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces. Comptes Rendus - Mecanique [Internet]. 2019 ;347:873-881. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075379471&doi=10.1016%2fj.crme.2019.11.012&partnerID=40&md5=dcb27af39dc14dc8c3a4a5f681f7d84b
Sartori A, Cammi A, Luzzi L, Rozza G. A multi-physics reduced order model for the analysis of Lead Fast Reactor single channel. Annals of Nuclear Energy, 87, 2 (2016): pp. 198-208 [Internet]. 2016 ;87:208. Available from: http://urania.sissa.it/xmlui/handle/1963/35191
Rozza G, Chen P, Quarteroni A. Multilevel and weighted reduced basis method for stochastic optimal control problems constrained by Stokes equations. Numerische Mathematik, (2015), 36 p. Article in Press [Internet]. 2015 . Available from: http://urania.sissa.it/xmlui/handle/1963/34491
Romor F, Tezzele M, Mrosek M, Othmer C, Rozza G. Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering. arXiv preprint arXiv:2110.14396. 2021 .
Nonino M, Ballarin F, Rozza G. A Monolithic and a Partitioned, Reduced Basis Method for Fluid–Structure Interaction Problems. Fluids [Internet]. 2021 ;6:229. Available from: https://www.mdpi.com/2311-5521/6/6/229
Strazzullo M, Ballarin F, Mosetti R, Rozza G. Model Reduction for Parametrized Optimal Control Problems in Environmental Marine Sciences and Engineering. SIAM Journal on Scientific Computing [Internet]. 2018 ;40:B1055-B1079. Available from: https://doi.org/10.1137/17M1150591
Benner P, Ohlberger M, Patera A, Rozza G, Sorensen DC, Urban K. Model order reduction of parameterized systems (MoRePaS): Preface to the special issue of advances in computational mathematics. Advances in Computational Mathematics. 2015 ;41:955–960.
Lassila T, Manzoni A, Quarteroni A, Rozza G. Model Order Reduction in Fluid Dynamics: Challenges and Perspectives. 2014 .
Hess M, Alla A, Quaini A, Rozza G, Gunzburger M. A Localized Reduced-Order Modeling Approach for PDEs with Bifurcating Solutions. Computer Methods in Applied Mechanics and Engineering [Internet]. 2019 ;351:379-403. Available from: https://arxiv.org/abs/1807.08851
Hess M, Alla A, Quaini A, Rozza G, Gunzburger M. A localized reduced-order modeling approach for PDEs with bifurcating solutions. Computer Methods in Applied Mechanics and Engineering [Internet]. 2019 ;351:379-403. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064313505&doi=10.1016%2fj.cma.2019.03.050&partnerID=40&md5=8b095034b9e539995facc7ce7bafa9e9
Jäggli C, Iapichino L, Rozza G. An improvement on geometrical parameterizations by transfinite maps. Comptes Rendus Mathematique. 2014 ;352:263–268.
Georgaka S, Stabile G, Star K, Rozza G, Bluck MJ. A hybrid reduced order method for modelling turbulent heat transfer problems. Computers & Fluids [Internet]. 2020 ;208:104615. Available from: https://arxiv.org/abs/1906.08725
Zancanaro M, Mrosek M, Stabile G, Othmer C, Rozza G. Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters. Fluids [Internet]. 2021 ;6:296. Available from: https://doi.org/10.3390/fluids6080296
Demo N, Tezzele M, Mola A, Rozza G. Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing. Journal of Marine Science and Engineering [Internet]. 2021 ;9:185. Available from: https://www.mdpi.com/2077-1312/9/2/185
Zancanaro M, Ballarin F, Perotto S, Rozza G. Hierarchical model reduction techniques for flow modeling in a parametrized setting. Multiscale Modeling and Simulation. 2021 ;19:267-293.
Salmoiraghi F, Scardigli A, Telib H, Rozza G. Free-form deformation, mesh morphing and reduced-order methods: enablers for efficient aerodynamic shape optimisation. International Journal of Computational Fluid Dynamics. 2018 ;32:233-247.
Koshakji A, Quarteroni A, Rozza G. Free Form Deformation Techniques Applied to 3D Shape Optimization Problems. Communications in Applied and Industrial Mathematics. 2013 .
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
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
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
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
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
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
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.
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.

Pages

Sign in