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. Model Order Reduction by means of Active Subspaces and Dynamic Mode Decomposition for Parametric Hull Shape Design Hydrodynamics. In: Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship & Maritime Research. Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship & Maritime Research. Trieste, Italy: IOS Press; 2018. Available from: http://ebooks.iospress.nl/publication/49270
. 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
. Model order reduction of parameterized systems (MoRePaS): Preface to the special issue of advances in computational mathematics. Advances in Computational Mathematics. 2015 ;41:955–960.
. Model Reduction for Parametrized Optimal Control Problems in Environmental Marine Sciences and Engineering. SIAM Journal on Scientific Computing [Internet]. 2018 ;40:B1055-B1079. Available from: https://doi.org/10.1137/17M1150591
. Model Reduction Methods. In: Encyclopedia of Computational Mechanics Second Edition. Encyclopedia of Computational Mechanics Second Edition. John Wiley & Sons; 2017. pp. 1-36.
. Model Reduction Using Sparse Polynomial Interpolation for the Incompressible Navier-Stokes Equations. 2022 .
. 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 .
. 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
. 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
. 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
. 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
. Non-intrusive data-driven ROM framework for hemodynamics problems. Acta Mechanica Sinica. 2021 ;37:1183–1191.
. 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
. 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
. 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
. 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
. An Online Stabilization Method for Parametrized Viscous Flows. In: Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators. Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators. Springer, Cham; 2024. Available from: https://link.springer.com/chapter/10.1007/978-3-031-55060-7_1
. 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
. An optimisation–based domain–decomposition reduced order model for parameter–dependent non–stationary fluid dynamics problems. Computers & Mathematics with Applications [Internet]. 2024 ;166:253-268. Available from: https://www.sciencedirect.com/science/article/pii/S0898122124002098