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Hess MW, Rozza G. Model Reduction Using Sparse Polynomial Interpolation for the Incompressible Navier-Stokes Equations. 2022 .
Chinesta F, Huerta A, Rozza G, Willcox K. Model Reduction Methods. In: Encyclopedia of Computational Mechanics Second Edition. Encyclopedia of Computational Mechanics Second Edition. John Wiley & Sons; 2017. pp. 1-36.
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 .
Khamlich M, Pichi F, Rozza G. Model order reduction for bifurcating phenomena in fluid-structure interaction problems. International Journal for Numerical Methods in FluidsInternational Journal for Numerical Methods in FluidsInt J Numer Meth Fluids [Internet]. 2022 ;n/a(n/a). Available from: https://doi.org/10.1002/fld.5118
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
Chinesta F, Huerta A, Rozza G, Willcox K. Model Order Reduction: a survey. In: Wiley Encyclopedia of Computational Mechanics, 2016. Wiley Encyclopedia of Computational Mechanics, 2016. Wiley; 2016. Available from: http://urania.sissa.it/xmlui/handle/1963/35194
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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Ngan VEric Brice, Stabile G, Mola A, Rozza G. A hybrid reduced-order model for segregated fluid-structure interaction solvers in an ALE approach at high Reynolds number.; 2024.
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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Padula G, Romor F, Stabile G, Rozza G. Generative models for the deformation of industrial shapes with linear geometric constraints: Model order and parameter space reductions. . Computer Methods in Applied Mechanics and Engineering [Internet]. 2024 ;423. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0045782524000793
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.
Romor F, Torlo D, Rozza G. Friedrichs' systems discretized with the Discontinuous Galerkin method: domain decomposable model order reduction and Graph Neural Networks approximating vanishing viscosity solutions.; 2023.

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