@article{BravoStabileHessHernandezRossi,
author = {Raul Bravo and Giovanni Stabile and Martin Hess and Joacquin Hernandez and Riccardo Rossi and Gianluigi Rozza},
journal = {Submitted},
title = {Geometrically Parametrised Reduced Order Models for the Study of Hysteresis of the Coanda Effect in Finite-elements-based Incompressible Fluid Dynamics},
year = {2023},
eprint = {arXiv:2307.05227},
eprinttype = {arXiv}
}
@article{BaSiGiStaPaPaSaRo2023,
author = {Caterina Balzotti and Pierfrancesco Siena and Michele Girfoglio and Giovanni Stabile and Jorge Dueñas-Pamplona and José Sierra-Pallares and Ignacio Amat-Santos and Gianluigi Rozza},
title = {{A Reduced Order Model formulation for left atrium flow: an Atrial Fibrillation case}},
year = {2023},
journal = {Submitted},
eprint = {arXiv:2309.10601},
eprinttype = {arXiv}
}
@article{BustoStabileRozzaCendon2019,
author = {Saray Busto and Giovanni Stabile and Gianluigi Rozza and Maria V{\'{a}}zquez-Cend{\'{o}}n},
journal = {Computers {\&} Mathematics with Applications},
title = {{POD}-Galerkin reduced order methods for combined Navier-Stokes transport equations based on a hybrid {FV}-{FE} solver},
year = {2019},
abstract = {The purpose of this work is to introduce a novel POD-Galerkin strategy for the hybrid finite volume/finite element solver introduced in Bermúdez et al. 2014 and Busto et al. 2018. The interest is into the incompressible Navier-Stokes equations coupled with an additional transport equation. The full order model employed in this article makes use of staggered meshes. This feature will be conveyed to the reduced order model leading to the definition of reduced basis spaces in both meshes. The reduced order model presented herein accounts for velocity, pressure, and a transport-related variable. The pressure term at both the full order and the reduced order level is reconstructed making use of a projection method. More precisely, a Poisson equation for pressure is considered within the reduced order model. Results are verified against three-dimensional manufactured test cases. Moreover a modified version of the classical cavity test benchmark including the transport of a species is analysed.},
archiveprefix = {arXiv},
doi = {10.1016/j.camwa.2019.06.026},
eprint = {1810.07999},
preprint = {https://arxiv.org/abs/1810.07999},
publisher = {Elsevier {BV}}
}
@article{CoMeDeStaRo2022,
author = {Dario Coscia and Laura Meneghetti and Nicola Demo and Giovanni Stabile and Gianluigi Rozza.},
journal = {Computational Mechanics},
title = {A continuous trainable filter for modelling unstructured data},
year = {2023},
pages = {1432-0924},
doi = {10.1007/s00466-023-02291-1}
}
@article{CraStaLaLaCaVaRo2022,
author = {Martina Cracco and Giovanni Stabile and Andrea Lario and Martin Larcher and Folco Casadei and Georgios Valsamos and Gianluigi Rozza},
journal = {Submitted},
title = {Deep learning-based reduced-order methods for fast transient dynamics},
year = {2022},
eprint = {arXiv:2212.07737},
eprinttype = {arxiv}
}
@article{GadallaCianferraTezzeleStabileMolaRozza2020,
author = {Mahmoud Gadalla and Marta Cianferra and Marco Tezzele and Giovanni Stabile and Andrea Mola and Gianluigi Rozza},
journal = {Computers {\&} Fluids},
title = {On the comparison of {LES} data-driven reduced order approaches for hydroacoustic analysis},
year = {2021},
pages = {104819},
volume = {216},
doi = {10.1016/j.compfluid.2020.104819},
publisher = {Elsevier {BV}},
url = {https://doi.org/10.1016/j.compfluid.2020.104819}
}
@article{GeorgakaStabileRozzaBluck2019,
author = {Sokratia Georgaka and Giovanni Stabile and Gianluigi Rozza and Michael J. Bluck},
journal = {Communications in Computational Physics},
title = {{Parametric POD-Galerkin Model Order Reduction for Unsteady-State Heat Transfer Problems}},
year = {2019},
issn = {1991-7120},
number = {1},
pages = {1--32},
volume = {27},
abstract = {A parametric reduced order model based on proper orthogonal decomposition with Galerkin projection has been developed and applied for the modeling of heat transport in T-junction pipes which are widely found in nuclear power plants. Thermal mixing of different temperature coolants in T-junction pipes leads to temperature fluctuations and this could potentially cause thermal fatigue in the pipe walls. The novelty of this paper is the development of a parametric ROM considering the three dimensional, incompressible, unsteady Navier-Stokes equations coupled with the heat transport equation in a finite volume approximation. Two different parametric cases are presented in this paper: parametrization of the inlet temperatures and parametrization of the kinematic viscosity. Different training spaces are considered and the results are compared against the full order model.},
archiveprefix = {arXiv},
doi = {10.4208/cicp.OA-2018-0207},
eprint = {1808.05175v2},
preprint = {https://arxiv.org/abs/1808.05175v2},
primaryclass = {physics.comp-ph}
}
@article{GeorgakaStabileStarRozzaBluck2020,
author = {Sokratia Georgaka and Giovanni Stabile and Kelbij Star and Gianluigi Rozza and Michael J. Bluck},
journal = {Computers \& Fluids},
title = {{A hybrid reduced order method for modelling turbulent heat transfer problems}},
year = {2020},
issn = {0045-7930},
pages = {104615},
volume = {208},
abstract = {A parametric, hybrid reduced order model approach based on the Proper Orthogonal Decomposition with both Galerkin projection and interpolation based on Radial Basis Functions method is presented. This method is tested against a case of turbulent non-isothermal mixing in a T-junction pipe, a common ow arrangement found in nuclear reactor cooling systems. The reduced order model is derived from the 3D unsteady, incompressible Navier-Stokes equations weakly coupled with the energy equation. For high Reynolds numbers, the eddy viscosity and eddy diffusivity are incorporated into the reduced order model with a Proper Orthogonal Decomposition (nested and standard) with Interpolation (PODI), where the interpolation is performed using Radial Basis Functions. The reduced order solver, obtained using a k-{\omega} SST URANS full order model, is tested against the full order solver in a 3D T-junction pipe with parametric velocity inlet boundary conditions.},
archiveprefix = {arXiv},
doi = {10.1016/j.compfluid.2020.104615},
eprint = {1906.08725},
preprint = {https://arxiv.org/abs/1906.08725}
}
@article{GiustiStabileMarinoBorri2017,
author = {Alessandro Giusti and Giovanni Stabile and Enzo Marino and Claudio Borri},
journal = {Procedia engineering},
title = {{Coupling effects on the dynamic response of moored floating platforms for offshore wind energy plants}},
year = {2017},
pages = {3194--3199},
volume = {199},
doi = {10.1016/j.proeng.2017.09.527},
publisher = {Elsevier}
}
@inproceedings{HijaziAliStabileBallarinRozza2020,
author = {Saddam Hijazi and Shafqat Ali and Giovanni Stabile and Francesco Ballarin and Gianluigi Rozza},
title = {{The Effort of Increasing Reynolds Number in Projection-Based Reduced Order Methods: from Laminar to Turbulent Flows}},
booktitle = {Lecture Notes in Computational Science and Engineering},
year = {2020},
pages = {245--264},
address = {Cham},
publisher = {Springer International Publishing},
abstract = {We present in this double contribution two different reduced order strategies for incompressible parameterized Navier-Stokes equations characterized by varying Reynolds numbers. The first strategy deals with low Reynolds number (laminar flow) and is based on a stabilized finite element method during the offline stage followed by a Galerkin projection on reduced basis spaces generated by a greedy algorithm. The second methodology is based on a full order finite volume discretization. The latter methodology will be used for flows with moderate to high Reynolds number characterized by turbulent patterns. For the treatment of the mentioned turbulent flows at the reduced order level, a new POD-Galerkin approach is proposed. The new approach takes into consideration the contribution of the eddy viscosity also during the online stage and is based on the use of interpolation. The two methodologies are tested on classic benchmark test cases.},
doi = {10.1007/978-3-030-30705-9_22},
eprint = {arXiv:1807.11370},
isbn = {978-3-030-30705-9},
preprint = {https://arxiv.org/abs/1807.11370}
}
@inbook{HijaziStabileMolaRozza2020a,
author = {Saddam Hijazi and Giovanni Stabile and Andrea Mola and Gianluigi Rozza},
pages = {217--240},
publisher = {Springer International Publishing},
title = {Non-intrusive Polynomial Chaos Method Applied to Full-Order and Reduced Problems in Computational Fluid Dynamics: A Comparison and Perspectives},
year = {2020},
address = {Cham},
isbn = {978-3-030-48721-8},
abstract = {In this work, Uncertainty Quantification (UQ) based on non-intrusive Polynomial Chaos Expansion (PCE) is applied to the CFD problem of the flow past an airfoil with parameterized angle of attack and inflow velocity. To limit the computational cost associated with each of the simulations required by the non-intrusive UQ algorithm used, we resort to a Reduced Order Model (ROM) based on Proper Orthogonal Decomposition (POD)-Galerkin approach. A first set of results is presented to characterize the accuracy of the POD-Galerkin ROM developed approach with respect to the Full Order Model (FOM) solver (OpenFOAM). A further analysis is then presented to assess how the UQ results are affected by substituting the FOM predictions with the surrogate ROM ones.},
booktitle = {Quantification of Uncertainty: Improving Efficiency and Technology: QUIET selected contributions},
doi = {10.1007/978-3-030-48721-8_10},
eprint = {1901.02285},
preprint = {https://arxiv.org/abs/1901.02285},
url = {https://doi.org/10.1007/978-3-030-48721-8_10}
}
@article{HijaziStabileMolaRozza2020b,
author = {Saddam Hijazi and Giovanni Stabile and Andrea Mola and Gianluigi Rozza},
journal = {Journal of Computational Physics},
title = {{Data-Driven POD–Galerkin reduced order model for turbulent flows}},
year = {2020},
issn = {0021-9991},
pages = {109513},
volume = {416},
abstract = {In this work we present a Reduced Order Model which is specifically designed to deal with turbulent flows in a finite volume setting. The method used to build the reduced order model is based on the idea of merging/combining projection-based techniques with data-driven reduction strategies. In particular, the work presents a mixed strategy that exploits a data-driven reduction method to approximate the eddy viscosity solution manifold and a classical POD-Galerkin projection approach for the velocity and the pressure fields, respectively. The newly proposed reduced order model has been validated on benchmark test cases in both steady and unsteady settings with Reynolds up to Re=O(10^5).},
archiveprefix = {arXiv},
doi = {10.1016/j.jcp.2020.109513},
eprint = {1907.09909},
preprint = {https://arxiv.org/abs/1907.09909}
}
@article{IvagnesStabileMolaIliescuRozza2022bis,
author = {Anna Ivagnes and Giovanni Stabile and Andrea Mola and Traian Iliescu and Gianluigi Rozza},
journal = {Applied Mathematics and Computation},
title = {Hybrid data-driven closure strategies for reduced order modeling},
year = {2023},
pages = {127920},
volume = {448},
doi = {10.1016/j.amc.2023.127920},
eprint = {arXiv:2207.10531},
publisher = {Elsevier {BV}},
url = {https://doi.org/10.1016%2Fj.amc.2023.127920}
}
@inproceedings{KaratzasStabileAtallahScovazziRozza2020,
author = {Efthymios N. Karatzas and Giovanni Stabile and Nabib Atallah and Guglielmo Scovazzi and Gianluigi Rozza},
booktitle = {IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22--25, 2018},
title = {{A Reduced Order Approach for the Embedded Shifted Boundary FEM and a Heat Exchange System on Parametrized Geometries}},
year = {2020},
address = {Cham},
editor = {Fehr, J{\"o}rg and Haasdonk, Bernard},
pages = {111--125},
publisher = {Springer International Publishing},
abstract = {A model order reduction technique is combined with an embedded boundary finite element method with a POD-Galerkin strategy. The proposed methodology is applied to parametrized heat transfer problems and we rely on a sufficiently refined shape-regular background mesh to account for parametrized geometries. In particular, the employed embedded boundary element method is the Shifted Boundary Method (SBM) recently proposed. This approach is based on the idea of shifting the location of true boundary conditions to a surrogate boundary, with the goal of avoiding cut cells near the boundary of the computational domain. This combination of methodologies has multiple advantages. In the first place, since the Shifted Boundary Method always relies on the same background mesh, there is no need to update the discretized parametric domain. Secondly, we avoid the treatment of cut cell elements, which usually need particular attention. Thirdly, since the whole background mesh is considered in the reduced basis construction, the SBM allows for a smooth transition of the reduced modes across the immersed domain boundary. The performances of the method are verified in two dimensional heat transfer numerical examples.},
doi = {10.1007/978-3-030-21013-7_8},
eprint = {arXiv:1807.07753},
isbn = {978-3-030-21013-7},
preprint = {https://arxiv.org/abs/1807.07753}
}
@article{KaratzasStabileNouveauScovazziRozza2019a,
author = {Efthymios N. Karatzas and Giovanni Stabile and Leo Nouveau and Guglielmo Scovazzi and Gianluigi Rozza},
journal = {Computer Methods in Applied Mechanics and Engineering},
title = {{A reduced basis approach for PDEs on parametrized geometries based on the shifted boundary finite element method and application to a Stokes flow}},
year = {2019},
pages = {568--587},
volume = {347},
abstract = {We propose a model order reduction technique integrating the Shifted Boundary Method (SBM) with a POD-Galerkin strategy. This approach allows to treat more complex parametrized domains in an efficient and straightforward way. The impact of the proposed approach is threefold.
First, problems involving parametrizations of complex geometrical shapes and/or large domain deformations can be efficiently solved at full-order by means of the SBM, an unfitted boundary method that avoids remeshing and the tedious handling of cut cells by introducing an approximate surrogate boundary.
Second, the computational effort is further reduced by the development of a reduced order model (ROM) technique based on a POD-Galerkin approach.
Third, the SBM provides a smooth mapping from the true to the surrogate domain, and for this reason, the stability and performance of the reduced order basis are enhanced. This feature is the net result of the combination of the proposed ROM approach and the SBM. Similarly, the combination of the SBM with a projection-based ROM gives the great advantage of an easy and fast to implement algorithm considering geometrical parametrization with large deformations. The transformation of each geometry to a reference geometry (morphing) is in fact not required.
These combined advantages will allow the solution of PDE problems more efficiently. We illustrate the performance of this approach on a number of two-dimensional Stokes flow problems.},
archiveprefix = {arXiv},
doi = {10.1016/j.cma.2018.12.040},
eprint = {1807.07790v4},
preprint = {https://arxiv.org/abs/1807.07790v4},
publisher = {North-Holland}
}
@article{KaratzasStabileNouveauScovazziRozza2019b,
author = {Efthymios N. Karatzas and Giovanni Stabile and Leo Nouveau and Guglielmo Scovazzi and Gianluigi Rozza},
journal = {Computer Methods in Applied Mechanics and Engineering},
title = {{A Reduced-Order Shifted Boundary Method for Parametrized incompressible Navier-Stokes equations}},
year = {2020},
issn = {0045-7825},
pages = {113273},
volume = {370},
abstract = {We investigate a projection-based reduced order model of the steady incompressible Navier–Stokes equations for moderate Reynolds numbers. In particular, we construct an “embedded” reduced basis space, by applying proper orthogonal decomposition to the Shifted Boundary Method, a high-fidelity embedded method recently developed. We focus on the geometrical parametrization through level-set geometries, using a fixed Cartesian background geometry and the associated mesh. This approach avoids both remeshing and the development of a reference domain formulation, as typically done in fitted mesh finite element formulations. Two-dimensional computational examples for one and three parameter dimensions are presented to validate the convergence and the efficacy of the proposed approach.},
archiveprefix = {arXiv},
doi = {10.1016/j.cma.2020.113273},
eprint = {1907.10549},
preprint = {https://arxiv.org/abs/1907.10549},
primaryclass = {math.NA}
}
@inproceedings{MarinoLugniStabileBorri2014,
author = {Enzo Marino and Claudio Lugni and Giovanni Stabile and Claudio Borri},
booktitle = {9th International Conference on Structural Dynamics (EURODYN 2014)},
title = {{Coupled dynamic simulations of offshore wind turbines using linear, weakly and fully nonlinear wave models: the limitations of the second-order wave theory}},
year = {2014}
}
@inproceedings{MarinoLugniStabileBorriManuel2014,
author = {Enzo Marino and Claudio Lugni and Giovanni Stabile and Claudio Borri and Lance Manuel},
booktitle = {XIII Conference of the Italian Association for Wind Engineering (In-Vento 2014)},
title = {{Coupled dynamic simulations of offshore wind turbines: influence of wave modelling on the fatigue load assesment}},
year = {2014}
}
@incollection{MarinoStabileBorriLugni2013,
author = {Enzo Marino and Giovanni Stabile and Claudio Borri and Claudio Lugni},
booktitle = {Research and Applications in Structural Engineering, Mechanics and Computation},
publisher = {CRC Press},
title = {{A comparative study about the effects of linear, weakly and fully nonlinear wave models on the dynamic response of offshore wind turbines}},
year = {2013},
pages = {389--390}
}
@article{MorelliBarralQuintelaRozzaStabile2021,
author = {Umberto Emil Morelli and Patricia Barral and Peregrina Quintela and Gianluigi Rozza and Giovanni Stabile},
journal = {International Journal for Numerical Methods in Engineering},
title = {A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation},
year = {2021},
number = {17},
pages = {4541--4574},
volume = {122},
doi = {10.1002/nme.6713},
publisher = {Wiley},
url = {https://doi.org/10.1002/nme.6713}
}
@article{PapapiccoDemoGirfoglioStabileRozza2021,
author = {Davide Papapicco and Nicola Demo and Michele Girfoglio and Giovanni Stabile and Gianluigi Rozza},
journal = {Computer Methods in Applied Mechanics and Engineering},
title = {The Neural Network shifted-proper orthogonal decomposition: A machine learning approach for non-linear reduction of hyperbolic equations},
year = {2022},
pages = {114687},
volume = {392},
doi = {10.1016/j.cma.2022.114687},
publisher = {Elsevier {BV}}
}
@article{RomorStabileRozza2022,
author = {Francesco Romor and Giovanni Stabile and Gianluigi Rozza},
journal = {Journal of Scientific Computing},
title = {{Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method}},
year = {2023},
volume = {94},
number = {3},
eprint = {arXiv:2203.00360},
doi = {10.1007/s10915-023-02128-2},
url = {https://doi.org/10.1007%2Fs10915-023-02128-2}
}
@incollection{RozzaHessStabileTezzeleBallarin2020,
author = {Gianluigi Rozza and Martin Hess and Giovanni Stabile and Marco Tezzele and Francesco Ballarin},
booktitle = {Model Order Reduction, Volume 2 Snapshot-Based Methods and Algorithms},
publisher = {De Gruyter},
title = {Basic ideas and tools for projection-based model reduction of parametric partial differential equations},
year = {2020},
address = {Berlin, Boston},
isbn = {9783110671490},
pages = {1 - 47},
doi = {10.1515/9783110671490-001},
url = {https://www.degruyter.com/view/book/9783110671490/10.1515/9783110671490-001.xml}
}
@article{SalavatidezfouliStabileGirfoglioHajisharifiRozza,
author = {Sajad Salavatidezfouli and Arash Hajisharifi and Michele Girfoglio and Giovanni Stabile and Gianluigi Rozza},
journal = {Submitted},
title = {{Applicable Methodologies for the Mass Transfer Phenomenon in Tumble Dryers: A Review}},
year = {2023},
eprint = {arXiv:2304.03533},
eprinttype = {arXiv}
}
@article{StabileBallarinZuccarinoRozza2019,
author = {Giovanni Stabile and Francesco Ballarin and Giacomo Zuccarino and Gianluigi Rozza},
journal = {Advances in Computational Mathematics},
title = {{A reduced order variational multiscale approach for turbulent flows}},
year = {2019},
number = {5-6},
pages = {2349--2368},
volume = {45},
abstract = {The purpose of this work is to present a reduced order modeling framework for parametrized turbulent flows with moderately high Reynolds numbers within the variational multiscale (VMS) method. The Reduced Order Models (ROMs) presented in this manuscript are based on a POD-Galerkin approach with a VMS stabilization technique. Two different reduced order models are presented, which differ on the stabilization used during the Galerkin projection. In the first case the VMS stabilization method is used at both the full order and the reduced order level. In the second case, the VMS stabilization is used only at the full order level, while the projection of the standard Navier-Stokes equations is performed instead at the reduced order level. The former method is denoted as consistent ROM, while the latter is named non-consistent ROM, in order to underline the different choices made at the two levels. Particular attention is also devoted to the role of inf-sup stabilization by means of supremizers in ROMs based on a VMS formulation. Finally, the developed methods are tested on a numerical benchmark.},
archiveprefix = {arXiv},
doi = {10.1007/s10444-019-09712-x},
eprint = {1809.11101},
preprint = {https://arxiv.org/abs/1809.11101},
publisher = {Springer Science and Business Media {LLC}}
}
@article{StabileHijaziMolaLorenziRozza2017,
author = {Giovanni Stabile and Saddam Hijazi and Andrea Mola and Stefano Lorenzi and Gianluigi Rozza},
journal = {Communications in Applied and Industrial Mathematics},
title = {{POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: vortex shedding around a circular cylinder}},
year = {2017},
number = {1},
pages = {210-236},
volume = {8},
archiveprefix = {arXiv},
doi = {10.1515/caim-2017-0011},
eprint = {1701.03424v2}
}
@inproceedings{StabileMatthiesBorri2015,
author = {Giovanni Stabile and Matthies, Hermann G and Claudio Borri},
booktitle = {Computational Methods in Marine Engineering VI -- MARINE2015},
title = {{A Reduced Order Model for the Simulation of Mooring Cable Dynamics}},
year = {2015},
organization = {Salvatore, Francesco; Broglia, Riccardo; Muscari, Roberto},
pages = {387--400}
}
@article{StabileMatthiesBorri2018,
author = {Giovanni Stabile and Hermann G. Matthies and Claudio Borri},
journal = {Ocean Engineering},
title = {{A novel reduced order model for vortex induced vibrations of long flexible cylinders}},
year = {2018},
pages = {191--207},
volume = {156},
archiveprefix = {arXiv},
doi = {10.1016/j.oceaneng.2018.02.064},
eprint = {1802.09241v1},
publisher = {Elsevier {BV}}
}
@article{StabileRosic2020,
author = {Giovanni Stabile and Bojana Rosic},
journal = {Computers \& Fluids},
title = {{Bayesian identification of a projection-based reduced order model for computational fluid dynamics}},
year = {2020},
issn = {0045-7930},
pages = {104477},
volume = {201},
abstract = {In this paper we propose a Bayesian method as a numerical way to correct and stabilise projection-based reduced order models (ROM) in computational fluid dynamics problems. The approach is of hybrid type, and consists of the classical proper orthogonal decomposition driven Galerkin projection of the laminar part of the governing equations, and Bayesian identification of the correction term mimicking both the turbulence model and possible ROM-related instabilities given the full order data. In this manner the classical ROM approach is translated to the parameter identification problem on a set of nonlinear ordinary differential equations. Computationally the inverse problem is solved with the help of the Gauss-Markov-Kalman smoother in both ensemble and square-root polynomial chaos expansion forms. To reduce the dimension of the posterior space, a novel global variance based sensitivity analysis is proposed.},
archiveprefix = {arXiv},
doi = {10.1016/j.compfluid.2020.104477},
eprint = {1910.11576},
preprint = {https://arxiv.org/abs/1910.11576}
}
@article{StabileRozza2018,
author = {Giovanni Stabile and Gianluigi Rozza},
journal = {Computers {\&} Fluids},
title = {{Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier--Stokes equations}},
year = {2018},
pages = {273--284},
volume = {173},
doi = {10.1016/j.compfluid.2018.01.035},
publisher = {Elsevier {BV}},
url = {https://doi.org/10.1016/j.compfluid.2018.01.035}
}
@incollection{chap9_aroma_book,
doi = {10.1137/1.9781611977257.ch9},
url = {https://doi.org/10.1137/1.9781611977257.ch9},
year = {2022},
publisher = {Society for Industrial and Applied Mathematics},
pages = {203--222},
author = {Marco Tezzele and Nicola Demo and Giovanni Stabile and Gianluigi Rozza},
title = {Chapter 9: Nonintrusive Data-Driven Reduced Order Models in Computational Fluid Dynamics},
booktitle = {Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics}
}
@incollection{chap7_aroma_book,
doi = {10.1137/1.9781611977257.ch7},
url = {https://doi.org/10.1137/1.9781611977257.ch7},
year = {2022},
publisher = {Society for Industrial and Applied Mathematics},
pages = {141--164},
author = {Matteo Zancanaro and Saddam Hijazi and Umberto Morelli and Giovanni Stabile and Gianluigi Rozza},
title = {Chapter 7: Finite Volume-Based Reduced Order Models for Laminar Flows},
booktitle = {Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics}
}
@incollection{chap8_aroma_book,
doi = {10.1137/1.9781611977257.ch8},
url = {https://doi.org/10.1137/1.9781611977257.ch8},
year = {2022},
publisher = {Society for Industrial and Applied Mathematics},
pages = {165--202},
author = {Matteo Zancanaro and Saddam Hijazi and Michele Girfoglio and Andrea Mola and Giovanni Stabile and Gianluigi Rozza},
title = {Chapter 8: Finite Volume-Based Reduced Order Models for Turbulent Flows},
booktitle = {Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics}
}
@incollection{chap13_aroma_book,
doi = {10.1137/1.9781611977257.ch13},
url = {https://doi.org/10.1137/1.9781611977257.ch13},
year = {2022},
month = jan,
publisher = {Society for Industrial and Applied Mathematics},
pages = {265--282},
author = {Efthymios N. Karatzas and Giovanni Stabile and Francesco Ballarin and Gianluigi Rozza},
title = {Chapter 13: Reduced Basis, Embedded Methods, and Parametrized Level-Set Geometry},
booktitle = {Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics}
}
@incollection{chap19_aroma_book,
doi = {10.1137/1.9781611977257.ch19},
url = {https://doi.org/10.1137/1.9781611977257.ch19},
year = {2022},
month = jan,
publisher = {Society for Industrial and Applied Mathematics},
pages = {379--387},
author = {Nicola Demo and Marco Tezzele and Giovanni Stabile and Gianluigi Rozza},
title = {Chapter 19: Scientific Software Development and Packages for Reduced Order Models in Computational Fluid Dynamics},
booktitle = {Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics}
}
@incollection{chap20_aroma_book,
doi = {10.1137/1.9781611977257.ch20},
url = {https://doi.org/10.1137/1.9781611977257.ch20},
year = {2022},
month = jan,
publisher = {Society for Industrial and Applied Mathematics},
pages = {389--413},
author = {Laura Meneghetti and Nirav Shah and Michele Girfoglio and Nicola Demo and Marco Tezzele and Andrea Lario and Giovanni Stabile and Gianluigi Rozza},
title = {Chapter 20: A Deep Learning Approach to Improving Reduced Order Models},
booktitle = {Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics}
}
@article{StabileZancanaroRozza2020,
author = {Giovanni Stabile and Matteo Zancanaro and Gianluigi Rozza},
journal = {International Journal for Numerical Methods in Engineering},
title = {{Efficient Geometrical parametrization for finite-volume based reduced order methods}},
year = {2020},
number = {12},
pages = {2655-2682},
volume = {121},
abstract = {In this work, we present an approach for the efficient treatment of parametrized geometries in the context of POD-Galerkin reduced order methods based on Finite Volume full order approximations. On the contrary to what is normally done in the framework of finite element reduced order methods, different geometries are not mapped to a common reference domain: the method relies on basis functions defined on an average deformed configuration and makes use of the Discrete Empirical Interpolation Method (D-EIM) to handle together non-affinity of the parametrization and non-linearities. In the first numerical example, different mesh motion strategies, based on a Laplacian smoothing technique and on a Radial Basis Function approach, are analyzed and compared on a heat transfer problem. Particular attention is devoted to the role of the non-orthogonal correction. In the second numerical example the methodology is tested on a geometrically parametrized incompressible Navier--Stokes problem. In this case, the reduced order model is constructed following the same segregated approach used at the full order level},
archiveprefix = {arXiv},
doi = {10.1002/nme.6324},
eprint = {1901.06373v3},
preprint = {https://arxiv.org/abs/1901.06373v3}
}
@article{StarSanderseStabileRozzaDegroote2020,
author = {Sabrina Kelbij Star and Benjamin Sanderse and Giovanni Stabile and Gianluigi Rozza and Joris Degroote},
journal = {International Journal for Numerical Methods in Fluids},
title = {Reduced order models for the incompressible Navier-Stokes equations on collocated grids using a `discretize-then-project' approach},
year = {2021},
number = {8},
pages = {2694--2722},
volume = {93},
doi = {10.1002/fld.4994},
publisher = {Wiley},
url = {https://doi.org/10.1002/fld.4994}
}
@article{StarStabileBelloniRozzaDegroote2019,
author = {S. Kelbij Star and Giovanni Stabile and Francesco Belloni and Gianluigi Rozza and Joris Degroote},
journal = {Communications in Computational Physics},
title = {{A novel iterative penalty method to enforce boundary conditions in Finite Volume POD-Galerkin reduced order models for fluid dynamics problems}},
year = {2021},
number = {1},
pages = {34--66},
volume = {30},
abstract = {A Finite-Volume based POD-Galerkin reduced order model is developed for fluid dynamic problems where the (time-dependent) boundary conditions are controlled using two different boundary control strategies: the control function method, whose aim is to obtain homogeneous basis functions for the reduced basis space and the penalty method where the boundary conditions are enforced in the reduced order model using a penalty factor. The penalty method is improved by using an iterative solver for the determination of the penalty factor rather than tuning the factor with a sensitivity analysis or numerical experimentation. The boundary control methods are compared and tested for two cases: the classical lid driven cavity benchmark problem and a Y-junction flow case with two inlet channels and one outlet channel. The results show that the boundaries of the reduced order model can be controlled with the boundary control methods and the same order of accuracy is achieved for the velocity and pressure fields. Finally, the speedup ratio between the reduced order models and the full order model is of the order 1000 for the lid driven cavity case and of the order 100 for the Y-junction test case.},
archiveprefix = {arXiv},
doi = {10.4208/cicp.OA-2020-0059},
eprint = {1912.00825},
preprint = {https://arxiv.org/abs/1912.00825},
publisher = {Global Science Press}
}
@inproceedings{StarStabileGeorgakaBelloniRozzaDegroote2019,
author = {Kelbij Star and Giovanni Stabile and Sokratia Georgaka and Francesco Belloni and Gianluigi Rozza and Joris Degroote},
booktitle = {International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019},
title = {{POD-Galerkin Reduced Order Model of the Boussinesq Approximation for Buoyancy-Driven Enclosed Flows}},
year = {2019},
isbn = {9780894487699}
}
@article{StarStabileRozzaDegroote2020,
author = {Kelbij Star and Giovanni Stabile and Gianluigi Rozza and Joris Degroote},
journal = {Applied Mathematical Modelling},
title = {{A POD-Galerkin reduced order model of a turbulent convective buoyant flow of sodium over a backward-facing step}},
year = {2021},
pages = {486 - 503},
volume = {89},
abstract = {A Finite-Volume based POD-Galerkin reduced order modeling strategy for steady-state Reynolds averaged Navier--Stokes (RANS) simulation is extended for low-Prandtl number flow. The reduced order model is based on a full order model for which the effects of buoyancy on the flow and heat transfer are characterized by varying the Richardson number. The Reynolds stresses are computed with a linear eddy viscosity model. A single gradient diffusion hypothesis, together with a local correlation for the evaluation of the turbulent Prandtl number, is used to model the turbulent heat fluxes. The contribution of the eddy viscosity and turbulent thermal diffusivity fields are considered in the reduced order model with an interpolation based data-driven method. The reduced order model is tested for buoyancy-aided turbulent liquid sodium flow over a vertical backward-facing step with a uniform heat flux applied on the wall downstream of the step. The wall heat flux is incorporated with a Neumann boundary condition in both the full order model and the reduced order model. The velocity and temperature profiles predicted with the reduced order model for the same and new Richardson numbers inside the range of parameter values are in good agreement with the RANS simulations. Also, the local Stanton number and skin friction distribution at the heated wall are qualitatively well captured. Finally, the reduced order simulations, performed on a single core, are about $10^5$ times faster than the RANS simulations that are performed on eight cores.},
archiveprefix = {arXiv},
doi = {10.1016/j.apm.2020.07.029},
eprint = {2003.01114},
preprint = {https://arxiv.org/abs/2003.01114},
primaryclass = {physics.flu-dyn}
}
@article{TezzeleDemoStabileMolaRozza2020,
author = {Marco Tezzele and Nicola Demo and Giovanni Stabile and Andrea Mola and Gianluigi Rozza},
journal = {Advanced Modeling and Simulation in Engineering Sciences},
title = {{Enhancing CFD predictions in shape design problems by model and parameter space reduction}},
year = {2020},
number = {1},
volume = {7},
abstract = {In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD.},
archiveprefix = {arXiv},
doi = {10.1186/s40323-020-00177-y},
eprint = {2001.05237},
preprint = {https://arxiv.org/abs/2001.05237},
publisher = {Springer Science and Business Media {LLC}}
}
@article{ZancanaroMrosekStabileOthmerRozza2021,
author = {Matteo Zancanaro and Markus Mrosek and Giovanni Stabile and Carsten Othmer and Gianluigi Rozza},
journal = {Fluids},
title = {Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters},
year = {2021},
number = {8},
pages = {296},
volume = {6},
doi = {10.3390/fluids6080296},
publisher = {{MDPI} {AG}},
url = {https://doi.org/10.3390/fluids6080296}
}
@article{ZengStabileKaratzasScovazziRozza2022,
author = {Xianyi Zeng and Giovanni Stabile and Efthymios N. Karatzas and Guglielmo Scovazzi and Gianluigi Rozza},
journal = {Computer Methods in Applied Mechanics and Engineering},
title = {{Embedded domain Reduced Basis Models for the shallow water hyperbolic equations with the Shifted Boundary Method}},
year = {2022},
pages = {115143},
volume = {398},
archiveprefix = {arXiv},
doi = {10.1016/j.cma.2022.115143},
eprint = {2201.09546},
publisher = {Elsevier {BV}}
}
@article{ZancanaroStabileRozza2022,
author = {Matteo Zancanaro and Giovanni Stabile and Gianluigi Rozza},
journal = {Submitted},
title = {{A segregated reduced order model of a pressure-based solver for turbulentcompressible flows}},
year = {2022},
eprint = {arXiv:2205.09396},
eprinttype = {arxiv}
}
@article{IvagnesStabileMolaIliescuRozza2022,
author = {Anna Ivagnes and Giovanni Stabile and Andrea Mola and Traian Iliescu and Gianluigi Rozza},
journal = {Journal of Computational Physics},
title = {Pressure Data-Driven Variational Multiscale Reduced Order Models},
year = {2023},
month = jan,
pages = {111904},
doi = {10.1016/j.jcp.2022.111904},
publisher = {Elsevier {BV}},
url = {https://doi.org/10.1016/j.jcp.2022.111904}
}
@book{aroma_book,
editor = {Gianluigi Rozza and Giovanni Stabile and Francesco Ballarin},
publisher = {Society for Industrial and Applied Mathematics},
title = {Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics},
year = {2022},
doi = {10.1137/1.9781611977257},
url = {https://doi.org/10.1137/1.9781611977257}
}
@article{MoBaQuiRoSta2022,
doi = {10.1002/nme.7167},
url = {https://doi.org/10.1002/nme.7167},
year = {2022},
author = {Umberto Emil Morelli and Patricia Barral and Peregrina Quintela and Gianluigi Rozza and Giovanni Stabile},
title = {Novel Methodologies for Solving the Inverse Unsteady Heat Transfer Problem of Estimating the Boundary Heat Flux in Continuous Casting Molds},
journal = {International Journal for Numerical Methods in Engineering}
}
@article{TorloStabileRubinoRozza,
author = {Davide Torlo and Giovanni Stabile and Samuele Rubino and Gianluigi Rozza},
journal = {in Preparation},
title = {POD-Galerkin reduced order model for turbulent flows with Smagorinsky models},
year = {2022}
}
@article{SheSaStaRo2023,
author = {Armin Sheidani and Sajad Salavatidezfouli and Giovanni Stabile and Gianluigi Rozza},
journal = {Journal of Wind Engineering and Industrial Aerodynamics},
title = {Assessment of {URANS} and {LES} methods in predicting wake shed behind a vertical axis wind turbine},
year = {2023},
month = jan,
pages = {105285},
volume = {232},
doi = {10.1016/j.jweia.2022.105285},
publisher = {Elsevier {BV}},
url = {https://doi.org/10.1016/j.jweia.2022.105285}
}
@article{SheSaStaBaRo2023,
author = {Armin Sheidani and Sajad Salavatidezfouli and Giovanni Stabile and Mostafa Barzegar Gerdroodbary and Gianluigi Rozza},
journal = {Physics of Fluids},
title = {Assessment of icing effects on the wake shed behind a vertical axis wind turbine},
year = {2023},
issn = {1089-7666},
number = {9},
volume = {35},
doi = {10.1063/5.0169102},
publisher = {AIP Publishing}
}
@article{KhaStaRoKoHo2023,
author = {Khamlich, Moaad and Stabile, Giovanni and Rozza, Gianluigi and K\"{o}rnyei, László and Horváth, Zoltán},
journal = {Computer Methods in Applied Mechanics and Engineering},
title = {A physics-based reduced order model for urban air pollution prediction},
year = {2023},
pages = {116416},
volume = {417},
doi = {10.1016/j.cma.2023.116416},
eprint = {arXiv:2305.04575},
eprinttype = {arXiv},
publisher = {Elsevier BV}
}
@article{GoHeStaRo2023,
author = {Isabella Carla Gonnella and Martin W. Hess and Giovanni Stabile and Gianluigi Rozza},
journal = {Computers \& Mathematics with Applications},
title = {A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems},
year = {2023},
doi = {10.1016/j.camwa.2023.08.026},
eprint = {arXiv:2301.09926}
}
@article{NgaStaMoRo2023,
author = {Valentin Nkana Ngan and Giovanni Stabile and Andrea Mola and Gianluigi Rozza},
journal = {Submitted},
title = {A reduced-order model for segregated fluid-structure interaction solvers based on an ALE approach},
year = {2023},
eprint = {arXiv:2305.13613},
eprinttype = {arXiv}
}
@article{RoStaRo2023,
author = {Francesco Romor and Giovanni Stabile and Gianluigi Rozza},
journal = {Submitted},
title = {Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations},
year = {2023},
eprint = {arXiv:2308.03396},
eprinttype = {arXiv}
}
@article{LoBaJLStaIca2023,
author = {Tobias Long and Robert Barnett and Richard Jefferson-Loveday and Giovanni Stabile and Matteo Icardi},
journal = {Submitted},
title = {A novel reduced-order model for advection-dominated problems based on Radon-Cumulative-Distribution Transform},
year = {2023},
eprint = {arXiv:2304.14883},
eprinttype = {arXiv}
}
@article{PaRoStaRo2023,
author = {Guglielmo Padula and Francesco Romor and Giovanni Stabile and Gianluigi Rozza},
journal = {Submitted},
title = {Generative Models for the Deformation of Industrial Shapes with Linear Geometric Constraints: model order and parameter space reductions},
year = {2023},
eprint = {arXiv:2308.03662},
eprinttype = {arXiv}
}
@article{GiStaMoRo2023,
author = {Michele Girfoglio and Giovanni Stabile and Andrea Mola and Gianluigi Rozza},
journal = {Submitted},
title = {An efficient FV-based Virtual Boundary Method for the simulation of fluid-solid interaction},
year = {2021},
eprint = {arXiv:2110.11756},
eprinttype = {arxiv}
}
@misc{ithacafv,
author = {Giovanni Stabile},
howpublished = {\url{https://github.com/ITHACA-FV/ITHACA-FV}},
note = {Accessed: 12-10-2023},
title = {{ITHACA-FV}}
}
@article{HaStaRo24,
author = {Rahul Halder and Giovanni Stabile and Gianluigi Rozza},
journal = {Submitted},
title = {{Physics Informed Neural Network Framework for Unsteady Discretized Reduced Order System}},
year = {2024},
eprint = {arXiv:2311.14045},
eprinttype = {arXiv}
}
@article{SaSheBaSaHaStaRo24,
author = {Sajad Salavatidezfouli and Armin Sheidani and Kabir Bakhshaei and Ali Safari and Arash Hajisharifi and Giovanni Stabile and Gianluigi Rozza},
journal = {Submitted},
title = {{Modal Analysis of the Wake Shed Behind a Horizontal Axis Wind Turbine with Flexible Blades}},
year = {2024},
eprint = {arXiv:2311.08130},
eprinttype = {arXiv}
}
@article{Salavatidezfouli2023,
author = {Salavatidezfouli, Sajad and Zadeh, Saeid Moradi and Stabile, Giovanni and Rozza, Gianluigi},
journal = {Advances in Computational Science and Engineering},
title = {Deep reinforcement learning for the heat transfer control of pulsating impinging jets},
year = {2023},
issn = {2837-1739},
number = {4},
pages = {401–423},
volume = {1},
doi = {10.3934/acse.2023016},
publisher = {American Institute of Mathematical Sciences (AIMS)}
}