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Giuliani N, Hess MW, DeSimone A, Rozza G. MicroROM: An Efficient and Accurate Reduced Order Method to Solve Many-Query Problems in Micro-Motility. [Internet]. 2020 . Available from: https://arxiv.org/abs/2006.13836

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Hess MW, 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

Hess MW, 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

Romor F, Tezzele M, Rozza G. A local approach to parameter space reduction for regression and classification tasks. arXiv preprint arXiv:2107.10867. 2021 .

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Romor F, Tezzele M, Lario A, Rozza G. Kernel-based active subspaces with application to computational fluid dynamics parametric problems using discontinuous Galerkin method. International Journal for Numerical Methods in Engineering. 2022 ;123:6000-6027.

Romor F, Tezzele M, Lario A, Rozza G. Kernel-based Active Subspaces with application to CFD parametric problems using Discontinuous Galerkin method.; 2020.

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Salmoiraghi F, Ballarin F, Heltai L, Rozza G. Isogeometric analysis-based reduced order modelling for incompressible linear viscous flows in parametrized shapes. Springer, AMOS Advanced Modelling and Simulation in Engineering Sciences; 2016. Available from: http://urania.sissa.it/xmlui/handle/1963/35199

Jäggli C, Iapichino L, Rozza G. An improvement on geometrical parameterizations by transfinite maps. Comptes Rendus Mathematique. 2014 ;352:263–268.

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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.

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Lassila T, Manzoni A, Quarteroni A, Rozza G. Generalized reduced basis methods and n-width estimates for the approximation of the solution manifold of parametric PDEs. In: Springer, Indam Series, Vol. 4, 2012. Springer, Indam Series, Vol. 4, 2012. Springer; 2012. Available from: http://hdl.handle.net/1963/6340

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Rozza G. Fundamentals of Reduced Basis Method for problems governed by parametrized PDEs and applications. In: Separated representations and PGD-based model reduction : fundamentals and applications. Vol. 554. Separated representations and PGD-based model reduction : fundamentals and applications. Wien: Springer; 2014.

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

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

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

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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

Demo N, Strazzullo M, Rozza G. AN EXTENDED PHYSICS INFORMED NEURAL NETWORK FOR PRELIMINARY ANALYSIS OF PARAMETRIC OPTIMAL CONTROL PROBLEMS. 2021 .

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

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.

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