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. 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.
. Hierarchical model reduction techniques for flow modeling in a parametrized setting. Multiscale Modeling and Simulation. 2021 ;19:267-293.
. 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
. 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
. A local approach to parameter space reduction for regression and classification tasks. arXiv preprint arXiv:2107.10867. 2021 .
. 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
. 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.
. Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering. arXiv preprint arXiv:2110.14396. 2021 .
. Non-intrusive data-driven ROM framework for hemodynamics problems. Acta Mechanica Sinica. 2021 ;37:1183–1191.
. 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.
. 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
. 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
. 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.
. PyGeM: Python Geometrical Morphing. Software Impacts. 2021 ;7:100047.
. 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
. 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
. 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. 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
. 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
. 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
. 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
. 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
. 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
. 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