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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
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
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Papapicco D, Demo N, Girfoglio M, Stabile G, Rozza G. The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equations. 2021 .
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
Girfoglio M, Scandurra L, Ballarin F, Infantino G, Nicolò F, Montalto A, Rozza G, Scrofani R, Comisso M, Musumeci F. Non-intrusive data-driven ROM framework for hemodynamics problems. Acta Mechanica Sinica. 2021 ;37:1183–1191.
Hijazi S, Stabile G, Mola A, Rozza G. Non-intrusive Polynomial Chaos Method Applied to Full-Order and Reduced Problems in Computational Fluid Dynamics: A Comparison and Perspectives. In: Quantification of Uncertainty: Improving Efficiency and Technology: QUIET selected contributions. Quantification of Uncertainty: Improving Efficiency and Technology: QUIET selected contributions. Cham: Springer International Publishing; 2020. pp. 217–240. Available from: https://doi.org/10.1007/978-3-030-48721-8_10
Star K, Stabile G, Belloni F, Rozza G, Degroote J. A novel iterative penalty method to enforce boundary conditions in Finite Volume POD-Galerkin reduced order models for fluid dynamics problems. Communications in Computational Physics. 2021 ;30:34–66.
Morelli UEmil, Barral P, Quintela P, Rozza G, Stabile G. A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation. International Journal for Numerical Methods in Engineering [Internet]. 2021 ;122:4541–4574. Available from: https://doi.org/10.1002/nme.6713
Ballarin F, Faggiano E, Manzoni A, Quarteroni A, Rozza G, Ippolito S, Scrofani R. Numerical modeling of hemodynamics scenarios of patient-specific coronary artery bypass grafts. Biomechanics and Modeling in Mechanobiology [Internet]. 2017 ;16:1373-1399. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015065851&doi=10.1007%2fs10237-017-0893-7&partnerID=40&md5=c388f20bd5de14187bad9ed7d9affbd0
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Georgaka S, Stabile G, Rozza G, Bluck MJ. Parametric POD-Galerkin Model Order Reduction for Unsteady-State Heat Transfer Problems. Communications in Computational Physics [Internet]. 2019 ;27:1–32. Available from: https://arxiv.org/abs/1808.05175
Lorenzi S, Cammi A, Luzzi L, Rozza G. POD-Galerkin Method for Finite Volume Approximation of Navier-Stokes and RANS Equations. 2016 .
Strazzullo M, Ballarin F, Rozza G. POD-Galerkin Model Order Reduction for Parametrized Nonlinear Time Dependent Optimal Flow Control: an Application to Shallow Water Equations. 2020 .
Stabile G, Hijazi S, Mola A, Lorenzi S, Rozza G. POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: vortex shedding around a circular cylinder. Communications in Applied and Industrial Mathematics. 2017 ;8:210-236.
Girfoglio M, Quaini A, Rozza G. A POD-Galerkin reduced order model for a LES filtering approach. Journal of Computational Physics [Internet]. 2021 ;436. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102138957&doi=10.1016%2fj.jcp.2021.110260&partnerID=40&md5=73115708267e80754f343561c26f4744
Star K, Stabile G, Rozza G, Degroote J. A POD-Galerkin reduced order model of a turbulent convective buoyant flow of sodium over a backward-facing step. Applied Mathematical Modelling. 2021 ;89:486-503.
Star K, Stabile G, Georgaka S, Belloni F, Rozza G, Degroote J. POD-Galerkin Reduced Order Model of the Boussinesq Approximation for Buoyancy-Driven Enclosed Flows. In: International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019. International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019. ; 2019.
Ballarin F, D'Amario A, Perotto S, Rozza G. A POD-selective inverse distance weighting method for fast parametrized shape morphing. International Journal for Numerical Methods in Engineering [Internet]. 2019 ;117:860-884. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056396233&doi=10.1002%2fnme.5982&partnerID=40&md5=6aabcbdc9a0da25e36575a0ebfac034f
Strazzullo M, Ballarin F, Rozza G. POD–Galerkin Model Order Reduction for Parametrized Time Dependent Linear Quadratic Optimal Control Problems in Saddle Point Formulation. Journal of Scientific Computing. 2020 ;83.
Ballarin F, Rozza G. POD–Galerkin monolithic reduced order models for parametrized fluid-structure interaction problems. International Journal Numerical Methods for Fluids. 2016 .
Busto S, Stabile G, Rozza G, Vázquez-Cendón ME. POD–Galerkin reduced order methods for combined Navier–Stokes transport equations based on a hybrid FV-FE solver. Computers and Mathematics with Applications [Internet]. 2020 ;79:256-273. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068068567&doi=10.1016%2fj.camwa.2019.06.026&partnerID=40&md5=a8dcce1b53b8ee872d174bbc4c20caa3
Nonino M, Ballarin F, Rozza G, Maday Y. Projection based semi–implicit partitioned Reduced Basis Method for non parametrized and parametrized Fluid–Structure Interaction problems. 2022 .
Karatzas EN, Ballarin F, Rozza G. Projection-based reduced order models for a cut finite element method in parametrized domains. Computers and Mathematics with Applications [Internet]. 2020 ;79:833-851. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070900852&doi=10.1016%2fj.camwa.2019.08.003&partnerID=40&md5=2d222ab9c7832955d155655d3c93e1b1
Demo N, Tezzele M, Rozza G. PyDMD: Python Dynamic Mode Decomposition. The Journal of Open Source Software [Internet]. 2018 ;3:530. Available from: https://joss.theoj.org/papers/734e4326edd5062c6e8ee98d03df9e1d
Tezzele M, Demo N, Mola A, Rozza G. PyGeM: Python Geometrical Morphing. Software Impacts. 2021 ;7:100047.
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Sartori A, Cammi A, Luzzi L, Rozza G. A Reduced Basis Approach for Modeling the Movement of Nuclear Reactor Control Rods. NERS-14-1062; ASME J of Nuclear Rad Sci, 2, 2 (2016) 021019 [Internet]. 2016 ;2(2):8. Available from: http://urania.sissa.it/xmlui/handle/1963/35192

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