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Filters: Author is Gianluigi Rozza  [Clear All Filters]
2021
Romor F, Tezzele M, Rozza G. ATHENA: Advanced Techniques for High dimensional parameter spaces to Enhance Numerical Analysis. Software Impacts. 2021 ;10:100133.
Gadalla M, Cianferra M, Tezzele M, Stabile G, Mola A, Rozza G. On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis. Computers & Fluids [Internet]. 2021 ;216:104819. Available from: https://www.sciencedirect.com/science/article/pii/S0045793020303893
Pintore M, Pichi F, Hess M, Rozza G, Canuto C. Efficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method. Advances in Computational Mathematics. 2021 ;47.
Demo N, Ortali G, Gustin G, Rozza G, Lavini G. An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques. Bolletino dell Unione Matematica Italiana. 2021 ;14:211-230.
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.
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, 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
Romor F, Tezzele M, Rozza G. A local approach to parameter space reduction for regression and classification tasks. arXiv preprint arXiv:2107.10867. 2021 .
Nonino M, Ballarin F, Rozza G. A Monolithic and a Partitioned, Reduced Basis Method for Fluid–Structure Interaction Problems. Fluids [Internet]. 2021 ;6:229. Available from: https://www.mdpi.com/2311-5521/6/6/229
Romor F, Tezzele M, Rozza G. Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces. In: Proceedings in Applied Mathematics & Mechanics. Vol. 20. Proceedings in Applied Mathematics & Mechanics. Wiley Online Library; 2021.
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 .
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
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.
Tezzele M, Demo N, Mola A, Rozza G. PyGeM: Python Geometrical Morphing. Software Impacts. 2021 ;7:100047.
Karatzas EN, Nonino M, Ballarin F, Rozza G. A Reduced Order Cut Finite Element method for geometrically parametrized steady and unsteady Navier–Stokes problems. Computer & Mathematics With Applications [Internet]. 2021 . Available from: https://www.sciencedirect.com/science/article/pii/S0898122121002790
Strazzullo M, Zainib Z, Ballarin F, Rozza G. Reduced Order Methods for Parametrized Non-linear and Time Dependent Optimal Flow Control Problems, Towards Applications in Biomedical and Environmental Sciences. In: Numerical Mathematics and Advanced Applications ENUMATH 2019. Vol. 139. Numerical Mathematics and Advanced Applications ENUMATH 2019. Springer; 2021. pp. 841–850. Available from: https://arxiv.org/abs/1912.07886
Strazzullo M, Zainib Z, Ballarin F, Rozza G. Reduced Order Methods for Parametrized Non-linear and Time Dependent Optimal Flow Control Problems, Towards Applications in Biomedical and Environmental Sciences. In: Vermolen FJ, Vuik C Numerical Mathematics and Advanced Applications ENUMATH 2019. Numerical Mathematics and Advanced Applications ENUMATH 2019. Cham: Springer International Publishing; 2021. Available from: https://www.springerprofessional.de/en/reduced-order-methods-for-parametrized-non-linear-and-time-depen/19122676
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
2020
Rozza G, Malik MH, Demo N, Tezzele M, Girfoglio M, Stabile G, Mola A. Advances in reduced order methods for parametric industrial problems in computational fluid dynamics. In: Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018. Proceedings of the 6th European Conference on Computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th European Conference on Computational Fluid Dynamics, ECFD 2018. ; 2020. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075395686&partnerID=40&md5=fb0b1a3cfdfd35a104db9921bc9be675
Rozza G, Hess M, Stabile G, Tezzele M, Ballarin F. Basic ideas and tools for projection-based model reduction of parametric partial differential equations. In: Model Order Reduction, Volume 2 Snapshot-Based Methods and Algorithms. Model Order Reduction, Volume 2 Snapshot-Based Methods and Algorithms. Berlin, Boston: De Gruyter; 2020. pp. 1 - 47. Available from: https://www.degruyter.com/view/book/9783110671490/10.1515/9783110671490-001.xml
Ballarin F, Rebollo TC, Ávila ED, Marmol MG, Rozza G. Certified Reduced Basis VMS-Smagorinsky model for natural convection flow in a cavity with variable height. Computers and Mathematics with Applications [Internet]. 2020 ;80:973-989. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085843368&doi=10.1016%2fj.camwa.2020.05.013&partnerID=40&md5=7c6596865ec89651319c7dd97159dd77
Hijazi S, Stabile G, Mola A, Rozza G. Data-driven POD-Galerkin reduced order model for turbulent flows. Journal of Computational Physics [Internet]. 2020 ;416:109513. Available from: https://arxiv.org/abs/1907.09909

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