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Filters: Author is Gianluigi Rozza  [Clear All Filters]
Unpublished
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 .
Hess MW, Rozza G. Model Reduction Using Sparse Polynomial Interpolation for the Incompressible Navier-Stokes Equations. 2022 .
Romor F, Tezzele M, Rozza G. A local approach to parameter space reduction for regression and classification tasks. arXiv preprint arXiv:2107.10867. 2021 .
Demo N, Strazzullo M, Rozza G. AN EXTENDED PHYSICS INFORMED NEURAL NETWORK FOR PRELIMINARY ANALYSIS OF PARAMETRIC OPTIMAL CONTROL PROBLEMS. 2021 .
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. 2021 .
Strazzullo M, Girfoglio M, Ballarin F, Iliescu T, Rozza G. Consistency of the full and reduced order models for Evolve-Filter-Relax Regularization of Convection-Dominated, Marginally-Resolved Flows. 2021 .
Strazzullo M, Ballarin F, Rozza G. A CERTIFIED REDUCED BASIS Method FOR LINEAR PARAMETRIZED PARABOLIC OPTIMAL CONTROL PROBLEMS IN SPACE-TIME FORMULATION. 2021 .
Pichi F, Ballarin F, Rozza G, Hesthaven JS. An artificial neural network approach to bifurcating phenomena in computational fluid dynamics. 2021 .
Preprint
Lassila T, Manzoni A, Rozza G. Reduction Strategies for Shape Dependent Inverse Problems in Haemodynamics. SISSA; 2013.
Lassila T, Manzoni A, Quarteroni A, Rozza G. A reduced-order strategy for solving inverse Bayesian identification problems in physiological flows. SISSA; 2013.
Lassila T, Manzoni A, Quarteroni A, Rozza G. A Reduced Computational and Geometrical Framework for Inverse Problems in Haemodynamics. SISSA; 2013.
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
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
Miscellaneous
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 .
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]. 2022 . Available from: https://arxiv.org/abs/2211.14528
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 .
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
Meneghetti L, Demo N, Rozza G. A Dimensionality Reduction Approach for Convolutional Neural Networks. 2021 .
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 .
Hess MW, Quaini A, Rozza G. Data-Driven Enhanced Model Reduction for Bifurcating Models in Computational Fluid Dynamics. 2022 .
Journal Article
Venturi L, Torlo D, Ballarin F, Rozza G. Weighted Reduced Order Methods for Parametrized Partial Differential Equations with Random Inputs. PoliTO Springer Series [Internet]. 2019 :27-40. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084009379&doi=10.1007%2f978-3-030-04870-9_2&partnerID=40&md5=446bcc1f331167bbba67bc00fb170150
Chen P, Quarteroni A, Rozza G. A weighted reduced basis method for elliptic partial differential equations with random input data. SIAM Journal on Numerical Analysis. 2013 ;51:3163–3185.
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
.Venturi L, Ballarin F, Rozza G. A Weighted POD Method for Elliptic PDEs with Random Inputs. Journal of Scientific Computing [Internet]. 2019 ;81:136-153. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053798049&doi=10.1007%2fs10915-018-0830-7&partnerID=40&md5=5cad501b6ef1955da55868807079ee5d

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