@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} } @booklet {2022, title = {Data-Driven Enhanced Model Reduction for Bifurcating Models in Computational Fluid Dynamics}, year = {2022}, author = {Martin W. Hess and Annalisa Quaini and Gianluigi Rozza} } @booklet {2022, title = {A Data-Driven Surrogate Modeling Approach for Time-Dependent Incompressible Navier-Stokes Equations with Dynamic Mode Decomposition and Manifold Interpolation}, year = {2022}, author = {Martin W. Hess and Annalisa Quaini and Gianluigi Rozza} } @article {2022, title = {A POD-Galerkin reduced order model for the Navier{\textendash}Stokes equations in stream function-vorticity formulation}, year = {2022}, month = {2022/06/14/}, pages = {105536}, abstract = {

We develop a Proper Orthogonal Decomposition (POD)-Galerkin based Reduced Order Model (ROM) for the efficient numerical simulation of the parametric Navier{\textendash}Stokes equations in the stream function-vorticity formulation. Unlike previous works, we choose different reduced coefficients for the vorticity and stream function fields. In addition, for parametric studies we use a global POD basis space obtained from a database of time dependent full order snapshots related to sample points in the parameter space. We test the performance of our ROM strategy with the well-known vortex merger benchmark and a more complex case study featuring the geometry of the North Atlantic Ocean. Accuracy and efficiency are assessed for both time reconstruction and physical parametrization.

}, keywords = {Galerkin projection, Navier{\textendash}Stokes equations, Proper orthogonal decomposition, Reduced order model, Stream function-vorticity formulation}, isbn = {0045-7930}, url = {https://www.sciencedirect.com/science/article/pii/S0045793022001645}, author = {Michele Girfoglio and Annalisa Quaini and Gianluigi Rozza} } @article {2021, title = {A POD-Galerkin reduced order model for a LES filtering approach}, journal = {Journal of Computational Physics}, volume = {436}, year = {2021}, abstract = {

We propose a Proper Orthogonal Decomposition (POD)-Galerkin based Reduced Order Model (ROM) for an implementation of the Leray model that combines a two-step algorithm called Evolve-Filter (EF) with a computationally efficient finite volume method. The main novelty of the proposed approach relies in applying spatial filtering both for the collection of the snapshots and in the reduced order model, as well as in considering the pressure field at reduced level. In both steps of the EF algorithm, velocity and pressure fields are approximated by using different POD basis and coefficients. For the reconstruction of the pressures fields, we use a pressure Poisson equation approach. We test our ROM on two benchmark problems: 2D and 3D unsteady flow past a cylinder at Reynolds number 0<=Re<=100. The accuracy of the reduced order model is assessed against results obtained with the full order model. For the 2D case, a parametric study with respect to the filtering radius is also presented.

}, doi = {10.1016/j.jcp.2021.110260}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102138957\&doi=10.1016\%2fj.jcp.2021.110260\&partnerID=40\&md5=73115708267e80754f343561c26f4744}, author = {Michele Girfoglio and Annalisa Quaini and Gianluigi Rozza} } @article {2020, title = {Reduced Basis Model Order Reduction for Navier-Stokes equations in domains with walls of varying curvature}, journal = {International Journal of Computational Fluid Dynamics}, volume = {34}, year = {2020}, pages = {119-126}, abstract = {

We consider the Navier-Stokes equations in a channel with a narrowing and walls of varying curvature. By applying the empirical interpolation method to generate an affine parameter dependency, the offline-online procedure can be used to compute reduced order solutions for parameter variations. The reduced order space is computed from the steady-state snapshot solutions by a standard POD procedure. The model is discretised with high-order spectral element ansatz functions, resulting in 4752 degrees of freedom. The proposed reduced order model produces accurate approximations of steady-state solutions for a wide range of geometries and kinematic viscosity values. 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 valve shape. Through our computational study, we found that the critical Reynolds number for the symmetry breaking increases as the wall curvature increases.

}, doi = {10.1080/10618562.2019.1645328}, url = {https://arxiv.org/abs/1901.03708}, author = {Martin W. Hess and Annalisa Quaini and Gianluigi Rozza} } @article {2020, title = {Reduced basis model order reduction for Navier{\textendash}Stokes equations in domains with walls of varying curvature}, journal = {International Journal of Computational Fluid Dynamics}, volume = {34}, year = {2020}, pages = {119-126}, abstract = {

We consider the Navier{\textendash}Stokes equations in a channel with a narrowing and walls of varying curvature. By applying the empirical interpolation method to generate an affine parameter dependency, the offline-online procedure can be used to compute reduced order solutions for parameter variations. The reduced-order space is computed from the steady-state snapshot solutions by a standard POD procedure. The model is discretised with high-order spectral element ansatz functions, resulting in 4752 degrees of freedom. The proposed reduced-order model produces accurate approximations of steady-state solutions for a wide range of geometries and kinematic viscosity values. 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 valve shape. Through our computational study, we found that the critical Reynolds number for the symmetry breaking increases as the wall curvature increases.

}, doi = {10.1080/10618562.2019.1645328}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085233294\&doi=10.1080\%2f10618562.2019.1645328\&partnerID=40\&md5=e2ed8f24c66376cdc8b5485aa400efb0}, author = {Martin W. Hess and Annalisa Quaini and Gianluigi Rozza} } @article {2020, title = {A reduced order modeling technique to study bifurcating phenomena: Application to the gross-pitaevskii equation}, journal = {SIAM Journal on Scientific Computing}, year = {2020}, abstract = {

We propose a computationally efficient framework to treat nonlinear partial differential equations having bifurcating solutions as one or more physical control parameters are varied. Our focus is on steady bifurcations. Plotting a bifurcation diagram entails computing multiple solutions of a parametrized, nonlinear problem, which can be extremely expensive in terms of computational time. In order to reduce these demanding computational costs, our approach combines a continuation technique and Newton{\textquoteright}s method with a reduced order modeling (ROM) technique, suitably supplemented with a hyperreduction method. To demonstrate the effectiveness of our ROM approach, we trace the steady solution branches of a nonlinear Schr{\"o}dinger equation, called the Gross{Pitaevskii equation, as one or two physical parameters are varied. In the two-parameter study, we show that our approach is 60 times faster in constructing a bifurcation diagram than a standard full order method.

}, doi = {10.1137/20M1313106}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096768803\&doi=10.1137\%2f20M1313106\&partnerID=40\&md5=47d6012d10854c2f9a04b9737f870592}, author = {Federico Pichi and Annalisa Quaini and Gianluigi Rozza} } @article {2020, title = {A Reduced Order technique to study bifurcating phenomena: application to the Gross-Pitaevskii equation}, journal = {SIAM Journal on Scientific Computing}, year = {2020}, abstract = {

We propose a computationally efficient framework to treat nonlinear partial differential equations having bifurcating solutions as one or more physical control parameters are varied. Our focus is on steady bifurcations. Plotting a bifurcation diagram entails computing multiple solutions of a parametrized, nonlinear problem, which can be extremely expensive in terms of computational time. In order to reduce these demanding computational costs, our approach combines a continuation technique and Newton{\textquoteright}s method with a Reduced Order Modeling (ROM) technique, suitably supplemented with a hyper-reduction method. To demonstrate the effectiveness of our ROM approach, we trace the steady solution branches of a nonlinear Schr{\"o}dinger equation, called Gross-Pitaevskii equation, as one or two physical parameters are varied. In the two parameter study, we show that our approach is 60 times faster in constructing a bifurcation diagram than a standard Full Order Method.

}, doi = {https://doi.org/10.1137/20M1313106}, url = {https://arxiv.org/abs/1907.07082}, author = {Federico Pichi and Annalisa Quaini and Gianluigi Rozza} } @article {2020, title = {A spectral element reduced basis method for navier{\textendash}stokes equations with geometric variations}, journal = {Lecture Notes in Computational Science and Engineering}, volume = {134}, year = {2020}, pages = {561-571}, abstract = {

We consider the Navier-Stokes equations in a channel with a narrowing of varying height. The model is discretized with high-order spectral element ansatz functions, resulting in 6372 degrees of freedom. The steady-state snapshot solutions define a reduced order space through a standard POD procedure. The reduced order space allows to accurately and efficiently evaluate the steady-state solutions for different geometries. In particular, we detail different aspects of implementing the reduced order model in combination with a spectral element discretization. It is shown that an expansion in element-wise local degrees of freedom can be combined with a reduced order modelling approach to enhance computational times in parametric many-query scenarios.

}, doi = {10.1007/978-3-030-39647-3_45}, author = {Martin W. Hess and Annalisa Quaini and Gianluigi Rozza} } @article {2019, title = {A Finite Volume approximation of the Navier-Stokes equations with nonlinear filtering stabilization}, journal = {Computers and Fluids}, volume = {187}, year = {2019}, pages = {27-45}, abstract = {

We consider a Leray model with a nonlinear differential low-pass filter for the simulation of incompressible fluid flow at moderately large Reynolds number (in the range of a few thousands) with under-refined meshes. For the implementation of the model, we adopt the three-step algorithm Evolve-Filter-Relax (EFR). The Leray model has been extensively applied within a Finite Element (FE) framework. Here, we propose to combine the EFR algorithm with a computationally efficient Finite Volume (FV) method. Our approach is validated against numerical data available in the literature for the 2D flow past a cylinder and against experimental measurements for the 3D fluid flow in an idealized medical device, as recommended by the U.S. Food and Drug Administration. We will show that for similar levels of mesh refinement FV and FE methods provide significantly different results. Through our numerical experiments, we are able to provide practical directions to tune the parameters involved in EFR algorithm. Furthermore, we are able to investigate the impact of mesh features (element type, non-orthogonality, local refinement, and element aspect ratio) and the discretization method for the convective term on the agreement between numerical solutions and experimental data.

}, doi = {10.1016/j.compfluid.2019.05.001}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065471890\&doi=10.1016\%2fj.compfluid.2019.05.001\&partnerID=40\&md5=c982371b5b5d4b5664a676902aaa60f4}, author = {Michele Girfoglio and Annalisa Quaini and Gianluigi Rozza} } @article {2019, title = {A Finite Volume approximation of the Navier-Stokes equations with nonlinear filtering stabilization}, journal = {Computers \& Fluids}, volume = {187}, year = {2019}, pages = {27-45}, abstract = {

We consider a Leray model with a nonlinear differential low-pass filter for the simulation of incompressible fluid flow at moderately large Reynolds number (in the range of a few thousands) with under-refined meshes. For the implementation of the model, we adopt the three-step algorithm Evolve-Filter-Relax (EFR). The Leray model has been extensively applied within a Finite Element (FE) framework. Here, we propose to combine the EFR algorithm with a computationally efficient Finite Volume (FV) method. Our approach is validated against numerical data available in the literature for the 2D flow past a cylinder and against experimental measurements for the 3D fluid flow in an idealized medical device, as recommended by the U.S. Food and Drug Administration. We will show that for similar levels of mesh refinement FV and FE methods provide significantly different results. Through our numerical experiments, we are able to provide practical directions to tune the parameters involved in the model. Furthermore, we are able to investigate the impact of mesh features (element type, non-orthogonality, local refinement, and element aspect ratio) and the discretization method for the convective term on the agreement between numerical solutions and experimental data.

}, doi = {10.1016/j.compfluid.2019.05.001}, url = {https://arxiv.org/abs/1901.05251}, author = {Michele Girfoglio and Annalisa Quaini and Gianluigi Rozza} } @article {2019, title = {A Localized Reduced-Order Modeling Approach for PDEs with Bifurcating Solutions}, journal = {Computer Methods in Applied Mechanics and Engineering}, volume = {351}, year = {2019}, pages = {379-403}, abstract = {

Reduced-order modeling (ROM) commonly refers to the construction, based on a few solutions (referred to as snapshots) of an expensive discretized partial differential equation (PDE), and the subsequent application of low-dimensional discretizations of partial differential equations (PDEs) that can be used to more efficiently treat problems in control and optimization, uncertainty quantification, and other settings that require multiple approximate PDE solutions. In this work, a ROM is developed and tested for the treatment of nonlinear PDEs whose solutions bifurcate as input parameter values change. In such cases, the parameter domain can be subdivided into subregions, each of which corresponds to a different branch of solutions. Popular ROM approaches such as proper orthogonal decomposition (POD), results in a global low-dimensional basis that does no respect not take advantage of the often large differences in the PDE solutions corresponding to different subregions. Instead, in the new method, the k-means algorithm is used to cluster snapshots so that within cluster snapshots are similar to each other and are dissimilar to those in other clusters. This is followed by the construction of local POD bases, one for each cluster. The method also can detect which cluster a new parameter point belongs to, after which the local basis corresponding to that cluster is used to determine a ROM approximation. Numerical experiments show the effectiveness of the method both for problems for which bifurcation cause continuous and discontinuous changes in the solution of the PDE.

}, doi = {10.1016/j.cma.2019.03.050}, url = {https://arxiv.org/abs/1807.08851}, author = {Martin W. Hess and Alla, Alessandro and Annalisa Quaini and Gianluigi Rozza and Max Gunzburger} } @article {2019, title = {A localized reduced-order modeling approach for PDEs with bifurcating solutions}, journal = {Computer Methods in Applied Mechanics and Engineering}, volume = {351}, year = {2019}, pages = {379-403}, abstract = {

Reduced-order modeling (ROM) commonly refers to the construction, based on a few solutions (referred to as snapshots) of an expensive discretized partial differential equation (PDE), and the subsequent application of low-dimensional discretizations of partial differential equations (PDEs) that can be used to more efficiently treat problems in control and optimization, uncertainty quantification, and other settings that require multiple approximate PDE solutions. Although ROMs have been successfully used in many settings, ROMs built specifically for the efficient treatment of PDEs having solutions that bifurcate as the values of input parameters change have not received much attention. In such cases, the parameter domain can be subdivided into subregions, each of which corresponds to a different branch of solutions. Popular ROM approaches such as proper orthogonal decomposition (POD), results in a global low-dimensional basis that does not respect the often large differences in the PDE solutions corresponding to different subregions. In this work, we develop and test a new ROM approach specifically aimed at bifurcation problems. In the new method, the k-means algorithm is used to cluster snapshots so that within cluster snapshots are similar to each other and are dissimilar to those in other clusters. This is followed by the construction of local POD bases, one for each cluster. The method also can detect which cluster a new parameter point belongs to, after which the local basis corresponding to that cluster is used to determine a ROM approximation. Numerical experiments show the effectiveness of the method both for problems for which bifurcation cause continuous and discontinuous changes in the solution of the PDE.

}, doi = {10.1016/j.cma.2019.03.050}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064313505\&doi=10.1016\%2fj.cma.2019.03.050\&partnerID=40\&md5=8b095034b9e539995facc7ce7bafa9e9}, author = {Martin W. Hess and Alla, Alessandro and Annalisa Quaini and Gianluigi Rozza and Max Gunzburger} } @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} }