. Generative models for the deformation of industrial shapes with linear geometric constraints: Model order and parameter space reductions. . Computer Methods in Applied Mechanics and Engineering [Internet]. 2024 ;423. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0045782524000793
. 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
. 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
. 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
. 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 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 of a turbulent convective buoyant flow of sodium over a backward-facing step. Applied Mathematical Modelling. 2021 ;89:486-503.
. 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
. 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
. Bayesian identification of a projection-based reduced order model for computational fluid dynamics. Computers & Fluids. 2020 ;201:104477.
. 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
. Efficient Geometrical parametrization for finite-volume based reduced order methods. International Journal for Numerical Methods in Engineering [Internet]. 2020 ;121:2655-2682. Available from: https://arxiv.org/abs/1901.06373
. The Effort of Increasing Reynolds Number in Projection-Based Reduced Order Methods: from Laminar to Turbulent Flows. In: Lecture Notes in Computational Science and Engineering. Lecture Notes in Computational Science and Engineering. Cham: Springer International Publishing; 2020. pp. 245–264.
. Enhancing CFD predictions in shape design problems by model and parameter space reduction. Advanced Modeling and Simulation in Engineering Sciences [Internet]. 2020 ;7(40). Available from: https://arxiv.org/abs/2001.05237
. A hybrid reduced order method for modelling turbulent heat transfer problems. Computers & Fluids [Internet]. 2020 ;208:104615. Available from: https://arxiv.org/abs/1906.08725
. 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
. POD–Galerkin reduced order methods for combined Navier–Stokes transport equations based on a hybrid FV-FE solver. Computers and Mathematics with Applications [Internet]. 2020 ;79:256-273. Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068068567&doi=10.1016%2fj.camwa.2019.06.026&partnerID=40&md5=a8dcce1b53b8ee872d174bbc4c20caa3