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Filters: Author is Gianluigi Rozza  [Clear All Filters]
2021
Star K, Sanderse B, Stabile G, Rozza G, Degroote J. Reduced order models for the incompressible Navier-Stokes equations on collocated grids using a `discretize-then-project' approach. International Journal for Numerical Methods in Fluids [Internet]. 2021 ;93:2694–2722. Available from: https://doi.org/10.1002/fld.4994
Demo N, Tezzele M, Rozza G. A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems. SIAM Journal on Scientific Computing [Internet]. 2021 ;43(3). Available from: https://arxiv.org/abs/2006.07282
Carere G, Strazzullo M, Ballarin F, Rozza G, Stevenson R. A weighted POD-reduction approach for parametrized PDE-constrained optimal control problems with random inputs and applications to environmental sciences. Computers and Mathematics with Applications [Internet]. 2021 ;102:261-276. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117948561&doi=10.1016%2fj.camwa.2021.10.020&partnerID=40&md5=cb57d59a6975a35315b2cf5d0e3a6001
2022
Hess MW, Quaini A, Rozza G. A comparison of reduced-order modeling approaches using artificial neural networks for PDEs with bifurcating solutions. ETNA - Electronic Transactions on Numerical Analysis. 2022 ;56:52–65.
Hess MW, Quaini A, Rozza G. Data-Driven Enhanced Model Reduction for Bifurcating Models in Computational Fluid Dynamics. 2022 .
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 .
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. ESAIM: M2AN [Internet]. 2022 ;56(4):1361 - 1400. Available from: https://doi.org/10.1051/m2an/2022044
Shah N, Girfoglio M, Quintela P, Rozza G, Lengomin A, Ballarin F, Barral P. Finite element based Model Order Reduction for parametrized one-way coupled steady state linear thermo-mechanical problems. Finite Elements in Analysis and Design [Internet]. 2022 ;212. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0168874X2200110X
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.
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
Hess MW, Rozza G. Model Reduction Using Sparse Polynomial Interpolation for the Incompressible Navier-Stokes Equations. 2022 .
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. Computer Methods in Applied Mechanics and Engineering [Internet]. 2022 ;392. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124488633&doi=10.1016%2fj.cma.2022.114687&partnerID=40&md5=12f82dcaba04c4a7c44f8e5b20101997
Girfoglio M, Quaini A, Rozza G. A POD-Galerkin reduced order model for the Navier–Stokes equations in stream function-vorticity formulation. [Internet]. 2022 :105536. Available from: https://www.sciencedirect.com/science/article/pii/S0045793022001645
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 .
Meneghetti L, Demo N, Rozza G. A Proper Orthogonal Decomposition Approach for Parameters Reduction of Single Shot Detector Networks. In: 2022 IEEE International Conference on Image Processing (ICIP). 2022 IEEE International Conference on Image Processing (ICIP). ; 2022.
Ngan VEric Brice, Stabile G, Mola A, Rozza G. A reduced-order model for segregated fluid-structure interaction solvers based on an ALE approach.; 2022.
Ballarin F, Rozza G, Strazzullo M. Space-time POD-Galerkin approach for parametric flow control. Handbook of Numerical Analysis. 2022 ;23.
Shah N, Girfoglio M, Rozza G. Thermomechanical Modelling for Industrial Applications. In: Progress in Industrial Mathematics at ECMI 2021. Progress in Industrial Mathematics at ECMI 2021. Online conference hosted by the Bergische Universität Wuppertal: Springer, Cham; 2022. Available from: https://link.springer.com/chapter/10.1007/978-3-031-11818-0_28
2023
Salavatidezfouli S, Hajisharifi S, Girfoglio M, Stabile G, Rozza G. Applicable Methodologies for the Mass Transfer Phenomenon in Tumble Dryers: A Review. 2023 .
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. In: 13th International Conference on Spectral and High Order Methods, ICOSAHOM 2021. 13th International Conference on Spectral and High Order Methods, ICOSAHOM 2021. ; 2023.
Meneghetti L, Demo N, Rozza G. A dimensionality reduction approach for convolutional neural networks. Applied Intelligence [Internet]. 2023 ;58:2818-2833. Available from: https://link.springer.com/article/10.1007/s10489-023-04730-1
Romor F, Stabile G, Rozza G. Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations.; 2023.
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.
Romor F, Stabile G, Rozza G. Non-linear manifold reduced-order models with convolutional autoencoders and reduced over-collocation method. Journal of Scientific Computing [Internet]. 2023 ;94(3). Available from: https://link.springer.com/article/10.1007/s10915-023-02128-2
Prusak I, Nonino M, Torlo D, Ballarin F, Rozza G. An optimisation–based domain–decomposition reduced order model for the incompressible Navier-Stokes equations. [Internet]. 2023 ;151:172 - 189. Available from: https://www.sciencedirect.com/science/article/pii/S0898122123004248

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