. 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 vanishing-inertia analysis for finite-dimensional rate-independent systems with nonautonomous dissipation and an application to soft crawlers. [Internet]. 2021 ;60(5):191. Available from: https://doi.org/10.1007/s00526-021-02067-6
. 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
. On the Approximation of Quasistatic Evolutions for the Debonding of a Thin Film via Vanishing Inertia and Viscosity. [Internet]. 2020 ;30(3):903 - 951. Available from: https://doi.org/10.1007/s00332-019-09595-8
. 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.
. 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 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
. 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
The deal.II library, Version 9.2. Journal of Numerical Mathematics. 2020 ;28:131–146.
. Efficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method. Advances in Computational Mathematics [Internet]. 2020 . Available from: https://arxiv.org/abs/1912.06089
. 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
. MicroROM: An Efficient and Accurate Reduced Order Method to Solve Many-Query Problems in Micro-Motility. [Internet]. 2020 . Available from: https://arxiv.org/abs/2006.13836
. 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 Model Order Reduction for Parametrized Time Dependent Linear Quadratic Optimal Control Problems in Saddle Point Formulation. Journal of Scientific Computing. 2020 ;83.