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
. Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems. Advanced Modeling and Simulation in Engineering Sciences. 2018 ;5:25.
. 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
. Computational reduction strategies for the detection of steady bifurcations in incompressible fluid-dynamics: Applications to Coanda effect in cardiology. Journal of Computational Physics. 2017 ;344:557.
. 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.
. 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
. Comparison of a Modal Method and a Proper Orthogonal Decomposition approach for multi-group time-dependent reactor spatial kinetics. Annals of Nuclear Energy [Internet]. 2014 ;71:229. Available from: http://urania.sissa.it/xmlui/handle/1963/35039
. Comparison between reduced basis and stochastic collocation methods for elliptic problems. [Internet]. 2014 . Available from: http://urania.sissa.it/xmlui/handle/1963/34727
. A combination between the reduced basis method and the ANOVA expansion: On the computation of sensitivity indices. Comptes Rendus Mathematique. Volume 351, Issue 15-16, August 2013, Pages 593-598 [Internet]. 2013 . Available from: http://hdl.handle.net/1963/7389
. On a certified smagorinsky reduced basis turbulence model. SIAM Journal on Numerical Analysis [Internet]. 2017 ;55:3047-3067. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039928218&doi=10.1137%2f17M1118233&partnerID=40&md5=221d9cd2bcc74121fcef93efd9d3d76c
. 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
. Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models. Journal of Scientific Computing [Internet]. 2018 ;74:197-219. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017156114&doi=10.1007%2fs10915-017-0430-y&partnerID=40&md5=023ef0bb95713f4442d1fa374c92a964
. Boundary control and shape optimization for the robust design of bypass anastomoses under uncertainty. Mathematical Modelling and Numerical Analysis, in press, 2012-13 [Internet]. 2012 . Available from: http://hdl.handle.net/1963/6337
. BladeX: Python Blade Morphing. The Journal of Open Source Software. 2019 ;4:1203.
. ATHENA: Advanced Techniques for High dimensional parameter spaces to Enhance Numerical Analysis. Software Impacts. 2021 ;10:100133.
. On the Application of Reduced Basis Methods to Bifurcation Problems in Incompressible Fluid Dynamics. Journal of Scientific Computing. 2017 .
. Reduction strategies for PDE-constrained oprimization problems in Haemodynamics. European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012) J. Eberhardsteiner et.al. (eds.), Vienna, Austria, 10-14 sept. 2012 [Internet]. 2012 . Available from: http://hdl.handle.net/1963/6338
. A reduced order model for multi-group time-dependent parametrized reactor spatial kinetics. 22nd International Conference on Nuclear Engineering ICONE22 [Internet]. 2014 :V005T17A048-V005T17A048. Available from: http://urania.sissa.it/xmlui/handle/1963/35123
. An efficient shape parametrisation by free-form deformation enhanced by active subspace for hull hydrodynamic ship design problems in open source environment. The 28th International Ocean and Polar Engineering Conference [Internet]. 2018 . Available from: https://www.onepetro.org/conference-paper/ISOPE-I-18-481
. 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
. Stabilized reduced basis method for parametrized scalar advection-diffusion problems at higher Péclet number: Roles of the boundary layers and inner fronts. In: 11th World Congress on Computational Mechanics, WCCM 2014, 5th European Conference on Computational Mechanics, ECCM 2014 and 6th European Conference on Computational Fluid Dynamics, ECFD 2014. 11th World Congress on Computational Mechanics, WCCM 2014, 5th European Conference on Computational Mechanics, ECCM 2014 and 6th European Conference on Computational Fluid Dynamics, ECFD 2014. ; 2014. pp. 5614–5624. Available from: https://infoscience.epfl.ch/record/203327/files/ECCOMAS_PP_GR.pdf
. SRTP 2.0 - The evolution of the safe return to port concept. In: Technology and Science for the Ships of the Future - Proceedings of NAV 2018: 19th International Conference on Ship and Maritime Research. Technology and Science for the Ships of the Future - Proceedings of NAV 2018: 19th International Conference on Ship and Maritime Research. ; 2018.
. Shape optimization through proper orthogonal decomposition with interpolation and dynamic mode decomposition enhanced by active subspaces. In: 8th International Conference on Computational Methods in Marine Engineering, MARINE 2019. 8th International Conference on Computational Methods in Marine Engineering, MARINE 2019. ; 2019. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075390244&partnerID=40&md5=3e1f2e9a2539d34594caff13766c94b8
. Shape Optimization by means of Proper Orthogonal Decomposition and Dynamic Mode Decomposition. 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/49229
. 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
.