@article {2022, title = {A comparison of reduced-order modeling approaches using artificial neural networks for PDEs with bifurcating solutions}, journal = {ETNA - Electronic Transactions on Numerical Analysis}, volume = {56}, year = {2022}, pages = {52{\textendash}65}, doi = {10.1553/etna_vol56s52}, author = {Martin W. Hess and Annalisa Quaini and Gianluigi Rozza} } @unpublished {2021, title = {A CERTIFIED REDUCED BASIS Method FOR LINEAR PARAMETRIZED PARABOLIC OPTIMAL CONTROL PROBLEMS IN SPACE-TIME FORMULATION}, year = {2021}, author = {Maria Strazzullo and Francesco Ballarin and Gianluigi Rozza} } @article {2021, title = {On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis}, journal = {Computers \& Fluids}, volume = {216}, year = {2021}, pages = {104819}, abstract = {

In this work, Dynamic Mode Decomposition (DMD) and Proper Orthogonal Decomposition (POD) methodologies are applied to hydroacoustic dataset computed using Large Eddy Simulation (LES) coupled with Ffowcs Williams and Hawkings (FWH) analogy. First, a low-dimensional description of the flow fields is presented with modal decomposition analysis. Sensitivity towards the DMD and POD bases truncation rank is discussed, and extensive dataset is provided to demonstrate the ability of both algorithms to reconstruct the flow fields with all the spatial and temporal frequencies necessary to support accurate noise evaluation. Results show that while DMD is capable to capture finer coherent structures in the wake region for the same amount of employed modes, reconstructed flow fields using POD exhibit smaller magnitudes of global spatiotemporal errors compared with DMD counterparts. Second, a separate set of DMD and POD modes generated using half the snapshots is employed into two data-driven reduced models respectively, based on DMD mid cast and POD with Interpolation (PODI). In that regard, results confirm that the predictive character of both reduced approaches on the flow fields is sufficiently accurate, with a relative superiority of PODI results over DMD ones. This infers that, discrepancies induced due to interpolation errors in PODI is relatively low compared with errors induced by integration and linear regression operations in DMD, for the present setup. Finally, a post processing analysis on the evaluation of FWH acoustic signals utilizing reduced fluid dynamic fields as input demonstrates that both DMD and PODI data-driven reduced models are efficient and sufficiently accurate in predicting acoustic noises.

}, keywords = {Dynamic mode decomposition, Ffowcs Williams and Hawkings, Hydroacoustics, Large eddy simulation, Model reduction, Proper orthogonal decomposition}, issn = {0045-7930}, doi = {https://doi.org/10.1016/j.compfluid.2020.104819}, url = {https://www.sciencedirect.com/science/article/pii/S0045793020303893}, author = {Mahmoud Gadalla and Marta Cianferra and Marco Tezzele and Giovanni Stabile and Andrea Mola and Gianluigi Rozza} } @unpublished {2021, title = {Consistency of the full and reduced order models for Evolve-Filter-Relax Regularization of Convection-Dominated, Marginally-Resolved Flows}, year = {2021}, author = {Maria Strazzullo and Michele Girfoglio and Francesco Ballarin and T. Iliescu and Gianluigi Rozza} } @article {2020, title = {Certified Reduced Basis VMS-Smagorinsky model for natural convection flow in a cavity with variable height}, journal = {Computers and Mathematics with Applications}, volume = {80}, year = {2020}, pages = {973-989}, abstract = {

In this work we present a Reduced Basis VMS-Smagorinsky Boussinesq model, applied to natural convection problems in a variable height cavity, in which the buoyancy forces are involved. We take into account in this problem both physical and geometrical parametrizations, considering the Rayleigh number as a parameter, so as the height of the cavity. We perform an Empirical Interpolation Method to approximate the sub-grid eddy viscosity term that lets us obtain an affine decomposition with respect to the parameters. We construct an a posteriori error estimator, based upon the Brezzi{\textendash}Rappaz{\textendash}Raviart theory, used in the greedy algorithm for the selection of the basis functions. Finally we present several numerical tests for different parameter configuration.

}, doi = {10.1016/j.camwa.2020.05.013}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085843368\&doi=10.1016\%2fj.camwa.2020.05.013\&partnerID=40\&md5=7c6596865ec89651319c7dd97159dd77}, author = {F. Ballarin and Rebollo, T.C. and E.D. {\'A}vila and Marmol, M.G. and Gianluigi Rozza} } @conference {2019, title = {A complete data-driven framework for the efficient solution of parametric shape design and optimisation in naval engineering problems}, booktitle = {VIII International Conference on Computational Methods in Marine Engineering}, year = {2019}, abstract = {

In the reduced order modeling (ROM) framework, the solution of a parametric partial differential equation is approximated by combining the high-fidelity solutions of the problem at hand for several properly chosen configurations. Examples of the ROM application, in the naval field, can be found in [31, 24]. Mandatory ingredient for the ROM methods is the relation between the high-fidelity solutions and the parameters. Dealing with geometrical parameters, especially in the industrial context, this relation may be unknown and not trivial (simulations over hand morphed geometries) or very complex (high number of parameters or many nested morphing techniques). To overcome these scenarios, we propose in this contribution an efficient and complete data-driven framework involving ROM techniques for shape design and optimization, extending the pipeline presented in [7]. By applying the singular value decomposition (SVD) to the points coordinates defining the hull geometry {\textendash}- assuming the topology is inaltered by the deformation {\textendash}-, we are able to compute the optimal space which the deformed geometries belong to, hence using the modal coefficients as the new parameters we can reconstruct the parametric formulation of the domain. Finally the output of interest is approximated using the proper orthogonal decomposition with interpolation technique. To conclude, we apply this framework to a naval shape design problem where the bulbous bow is morphed to reduce the total resistance of the ship advancing in calm water.

}, url = {https://arxiv.org/abs/1905.05982}, author = {Nicola Demo and Marco Tezzele and Andrea Mola and Gianluigi Rozza} } @conference {2019, title = {A complete data-driven framework for the efficient solution of parametric shape design and optimisation in naval engineering problems}, booktitle = {8th International Conference on Computational Methods in Marine Engineering, MARINE 2019}, year = {2019}, abstract = {

In the reduced order modeling (ROM) framework, the solution of a parametric partial differential equation is approximated by combining the high-fidelity solutions of the problem at hand for several properly chosen configurations. Examples of the ROM application, in the naval field, can be found in [31, 24]. Mandatory ingredient for the ROM methods is the relation between the high-fidelity solutions and the parameters. Dealing with geometrical parameters, especially in the industrial context, this relation may be unknown and not trivial (simulations over hand morphed geometries) or very complex (high number of parameters or many nested morphing techniques). To overcome these scenarios, we propose in this contribution an efficient and complete data-driven framework involving ROM techniques for shape design and optimization, extending the pipeline presented in [7]. By applying the singular value decomposition (SVD) to the points coordinates defining the hull geometry {\textemdash} assuming the topology is inaltered by the deformation {\textemdash}, we are able to compute the optimal space which the deformed geometries belong to, hence using the modal coefficients as the new parameters we can reconstruct the parametric formulation of the domain. Finally the output of interest is approximated using the proper orthogonal decomposition with interpolation technique. To conclude, we apply this framework to a naval shape design problem where the bulbous bow is morphed to reduce the total resistance of the ship advancing in calm water.

}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075342565\&partnerID=40\&md5=d76b8a1290053e7a84fb8801c0e6bb3d}, author = {Nicola Demo and Marco Tezzele and Andrea Mola and Gianluigi Rozza} } @article {2018, title = {Certified Reduced Basis Approximation for the Coupling of Viscous and Inviscid Parametrized Flow Models}, journal = {Journal of Scientific Computing}, volume = {74}, year = {2018}, pages = {197-219}, doi = {10.1007/s10915-017-0430-y}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017156114\&doi=10.1007\%2fs10915-017-0430-y\&partnerID=40\&md5=023ef0bb95713f4442d1fa374c92a964}, author = {Immanuel Martini and Bernard Haasdonk and Gianluigi Rozza} } @inbook {tezzele2018combined, title = {Combined parameter and model reduction of cardiovascular problems by means of active subspaces and POD-Galerkin methods}, booktitle = {Mathematical and Numerical Modeling of the Cardiovascular System and Applications}, year = {2018}, pages = {185{\textendash}207}, publisher = {Springer}, organization = {Springer}, author = {Marco Tezzele and F. Ballarin and Gianluigi Rozza} } @inbook {AuricchioContiLefieuxMorgantiRealiRozzaVeneziani2018, title = {Computational methods in cardiovascular mechanics}, booktitle = {Cardiovascular Mechanics}, year = {2018}, pages = {54}, publisher = {CRC Press}, organization = {CRC Press}, chapter = {Computational methods in cardiovascular mechanics}, abstract = {

The introduction of computational models in cardiovascular sciences has been progressively bringing new and unique tools for the investigation of the physiopathology. Together with the dramatic improvement of imaging and measuring devices on one side, and of computational architectures on the other one, mathematical and numerical models have provided a new, clearly noninvasive, approach for understanding not only basic mechanisms but also patient-specific conditions, and for supporting the design and the development of new therapeutic options. The terminology in silico is, nowadays, commonly accepted for indicating this new source of knowledge added to traditional in vitro and in vivo investigations. The advantages of in silico methodologies are basically the low cost in terms of infrastructures and facilities, the reduced invasiveness and, in general, the intrinsic predictive capabilities based on the use of mathematical models. The disadvantages are generally identified in the distance between the real cases and their virtual counterpart required by the conceptual modeling that can be detrimental for the reliability of numerical simulations.

}, url = {https://www.taylorfrancis.com/books/e/9781315280288/chapters/10.1201\%2Fb21917-5}, author = {Auricchio, Ferdinando and Conti, Michele and Lefieux, Adrian and Morganti, Simone and Alessandro Reali and Gianluigi Rozza and Veneziani, Alessandro}, editor = {Michel F. Labrosse} } @inbook {2017, title = {Certi fied Reduced Basis Method for Affinely Parametric Isogeometric Analysis NURBS Approximation}, booktitle = {Spectral and High Order Methods for Partial Differential Equations }, volume = { 119}, number = {LNCSE}, year = {2017}, publisher = {Springer}, organization = {Springer}, edition = {Bittencourt, Dumont, Hesthaven. (Eds).}, address = {Heildeberg}, abstract = {

In this work we apply reduced basis methods for parametric PDEs to an isogeometric formulation based on

NURBS. The motivation for this work is an integrated and complete work pipeline from CAD to parametrization

of domain geometry, then from full order to certified reduced basis solution. IsoGeometric Analysis

\ (IGA) is a growing research theme in scientic computing and computational mechanics, as well as reduced

basis methods for parametric PDEs. Their combination enhances the solution of some class of problems,

especially the ones characterized by parametrized geometries we introduced in this work. For a general

overview on Reduced Basis (RB) methods we recall [7, 15] and on IGA [3]. This work wants to demonstrate

that it is also possible for some class of problems to deal with ane geometrical parametrization combined

with a NURBS IGA formulation. This is what this work brings as original ingredients with respect to other

works dealing with reduced order methods and IGA (set in a non-affine formulation, and using a POD [2]\ 

sampling without certication: see for example for potential\ flows [12] and for Stokes\ flows [17]). In this work

we show a certication of accuracy and a complete integration between IGA formulation and parametric

certified greedy RB formulation. Section 2 recalls the abstract setting for parametrized PDEs, Section 3

recalls IGA setting, Section 4 deals with RB formulation, and Section 5 illustrates two numerical examples in heat transfer with different parametrization.

}, isbn = {978-3-319-65869-8}, author = {Denis Devaud and Gianluigi Rozza} } @article {2017, title = {On a certified smagorinsky reduced basis turbulence model}, journal = {SIAM Journal on Numerical Analysis}, volume = {55}, year = {2017}, pages = {3047-3067}, doi = {10.1137/17M1118233}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039928218\&doi=10.1137\%2f17M1118233\&partnerID=40\&md5=221d9cd2bcc74121fcef93efd9d3d76c}, author = {Rebollo, T.C. and E.D. {\'A}vila and Marmol, M.G. and F. Ballarin and Gianluigi Rozza} } @article {2017, title = {Computational reduction strategies for the detection of steady bifurcations in incompressible fluid-dynamics: Applications to Coanda effect in cardiology}, journal = {Journal of Computational Physics}, volume = {344}, year = {2017}, month = {09/2017}, pages = {557}, chapter = {534}, abstract = {

We focus on reducing the computational costs associated with the hydrodynamic stability of solutions of the incompressible Navier{\textendash}Stokes equations for a Newtonian and viscous fluid in contraction{\textendash}expansion channels. In particular, we are interested in studying steady bifurcations, occurring when non-unique stable solutions appear as physical and/or geometric control parameters are varied. The formulation of the stability problem requires solving an eigenvalue problem for a partial differential operator. An alternative to this approach is the direct simulation of the flow to characterize the asymptotic behavior of the solution. Both approaches can be extremely expensive in terms of computational time. We propose to apply Reduced Order Modeling (ROM) techniques to reduce the demanding computational costs associated with the detection of a type of steady bifurcations in fluid dynamics. The application that motivated the present study is the onset of asymmetries (i.e., symmetry breaking bifurcation) in blood flow through a regurgitant mitral valve, depending on the Reynolds number and the regurgitant mitral valve orifice shape.

}, keywords = {Parametrized Navier{\textendash}Stokes equations, Reduced basis method, Stability of flows, Symmetry breaking bifurcation}, doi = {https://doi.org/10.1016/j.jcp.2017.05.010}, author = {Giuseppe Pitton and Annalisa Quaini and Gianluigi Rozza} } @book {2015, title = {Certified Reduced Basis Methods for Parametrized Partial Differential Equations}, series = {Springer Briefs in Mathematics}, year = {2015}, pages = {135}, publisher = {Springer}, organization = {Springer}, edition = {1}, address = {Switzerland}, abstract = {

This book provides a thorough introduction to the mathematical and algorithmic aspects of certified reduced basis methods for parametrized partial differential equations. Central aspects ranging from model construction, error estimation and computational efficiency to empirical interpolation methods are discussed in detail for coercive problems. More advanced aspects associated with time-dependent problems, non-compliant and non-coercive problems and applications with geometric variation are also discussed as examples.

}, keywords = {a posteriori error bounds, empirical interpolation, parametrized partial differential equations, reduced basis methods, greedy algorithms}, isbn = {978-3-319-22469-5}, issn = {2191-8201}, author = {Jan S Hesthaven and Gianluigi Rozza and Benjamin Stamm} } @article {2014, title = {Comparison between reduced basis and stochastic collocation methods for elliptic problems}, number = {Journal of scientific computing;volume 59; issue 1; pages 187-216;}, year = {2014}, publisher = {Springer}, abstract = {The stochastic collocation method (Babu{\v s}ka et al. in SIAM J Numer Anal 45(3):1005-1034, 2007; Nobile et al. in SIAM J Numer Anal 46(5):2411-2442, 2008a; SIAM J Numer Anal 46(5):2309-2345, 2008b; Xiu and Hesthaven in SIAM J Sci Comput 27(3):1118-1139, 2005) has recently been applied to stochastic problems that can be transformed into parametric systems. Meanwhile, the reduced basis method (Maday et al. in Comptes Rendus Mathematique 335(3):289-294, 2002; Patera and Rozza in Reduced basis approximation and a posteriori error estimation for parametrized partial differential equations Version 1.0. Copyright MIT, http://augustine.mit.edu, 2007; Rozza et al. in Arch Comput Methods Eng 15(3):229-275, 2008), primarily developed for solving parametric systems, has been recently used to deal with stochastic problems (Boyaval et al. in Comput Methods Appl Mech Eng 198(41-44):3187-3206, 2009; Arch Comput Methods Eng 17:435-454, 2010). In this work, we aim at comparing the performance of the two methods when applied to the solution of linear stochastic elliptic problems. Two important comparison criteria are considered: (1), convergence results of the approximation error; (2), computational costs for both offline construction and online evaluation. Numerical experiments are performed for problems from low dimensions O (1) to moderate dimensions O (10) and to high dimensions O (100). The main result stemming from our comparison is that the reduced basis method converges better in theory and faster in practice than the stochastic collocation method for smooth problems, and is more suitable for large scale and high dimensional stochastic problems when considering computational costs.}, doi = {10.1007/s10915-013-9764-2}, url = {http://urania.sissa.it/xmlui/handle/1963/34727}, author = {Peng Chen and Alfio Quarteroni and Gianluigi Rozza} } @article {2014, title = {Comparison of a Modal Method and a Proper Orthogonal Decomposition approach for multi-group time-dependent reactor spatial kinetics}, journal = {Annals of Nuclear Energy}, volume = {71}, number = {Annals of Nuclear Energy;volume 71; pages 217-229;}, year = {2014}, month = {09/2014}, pages = {229}, publisher = {Elsevier}, chapter = {217}, abstract = {

In this paper, two modelling approaches based on a Modal Method (MM) and on the Proper Orthogonal Decomposition (POD) technique, for developing a control-oriented model of nuclear reactor spatial kinetics, are presented and compared. Both these methods allow developing neutronics description by means of a set of ordinary differential equations. The comparison of the outcomes provided by the two approaches focuses on the capability of evaluating the reactivity and the neutron flux shape in different reactor configurations, with reference to a TRIGA Mark II reactor. The results given by the POD-based approach are higher-fidelity with respect to the reference solution than those computed according to the MM-based approach, in particular when the perturbation concerns a reduced region of the core. If the perturbation is homogeneous throughout the core, the two approaches allow obtaining comparable accuracy results on the quantities of interest. As far as the computational burden is concerned, the POD approach ensures a better efficiency rather than direct Modal Method, thanks to the ability of performing a longer computation in the preprocessing that leads to a faster evaluation during the on-line phase.

}, doi = {10.1016/j.anucene.2014.03.043}, url = {http://urania.sissa.it/xmlui/handle/1963/35039}, author = {Alberto Sartori and Davide Baroli and Antonio Cammi and Davide Chiesa and Lelio Luzzi and Roberto R. Ponciroli and Ezio Previtali and Marco E. Ricotti and Gianluigi Rozza and Monica Sisti} } @article {2013, title = {A combination between the reduced basis method and the ANOVA expansion: On the computation of sensitivity indices}, journal = {Comptes Rendus Mathematique. Volume 351, Issue 15-16, August 2013, Pages 593-598}, year = {2013}, publisher = {Elsevier}, abstract = {

We consider a method to efficiently evaluate in a real-time context an output based on the numerical solution of a partial differential equation depending on a large number of parameters. We state a result allowing to improve the computational performance of a three-step RB-ANOVA-RB method. This is a combination of the reduced basis (RB) method and the analysis of variations (ANOVA) expansion, aiming at compressing the parameter space without affecting the accuracy of the output. The idea of this method is to compute a first (coarse) RB approximation of the output of interest involving all the parameter components, but with a large tolerance on the a posteriori error estimate; then, we evaluate the ANOVA expansion of the output and freeze the least important parameter components; finally, considering a restricted model involving just the retained parameter components, we compute a second (fine) RB approximation with a smaller tolerance on the a posteriori error estimate. The fine RB approximation entails lower computational costs than the coarse one, because of the reduction of parameter dimensionality. Our result provides a criterion to avoid the computation of those terms in the ANOVA expansion that are related to the interaction between parameters in the bilinear form, thus making the RB-ANOVA-RB procedure computationally more feasible.

}, keywords = {Partial differential equations}, doi = {10.1016/j.crma.2013.07.023}, url = {http://hdl.handle.net/1963/7389}, author = {Denis Devaud and Andrea Manzoni and Gianluigi Rozza} }