00570nas a2200133 4500008004100000245012300041210006900164300001200233490000700245100002100252700002100273700002100294856012100315 2022 eng d00aA comparison of reduced-order modeling approaches using artificial neural networks for PDEs with bifurcating solutions0 acomparison of reducedorder modeling approaches using artificial  a52–650 v561 aHess, Martin, W.1 aQuaini, Annalisa1 aRozza, Gianluigi uhttps://math.sissa.it/publication/comparison-reduced-order-modeling-approaches-using-artificial-neural-networks-pdes00502nas a2200109 4500008004100000245009600041210006900137100002100206700002100227700002100248856012300269 2022 eng d00aData-Driven Enhanced Model Reduction for Bifurcating Models in Computational Fluid Dynamics0 aDataDriven Enhanced Model Reduction for Bifurcating Models in Co1 aHess, Martin, W.1 aQuaini, Annalisa1 aRozza, Gianluigi uhttps://math.sissa.it/publication/data-driven-enhanced-model-reduction-bifurcating-models-computational-fluid-dynamics00568nas a2200109 4500008004100000245016300041210006900204100002100273700002100294700002100315856012200336 2022 eng d00aA Data-Driven Surrogate Modeling Approach for Time-Dependent Incompressible Navier-Stokes Equations with Dynamic Mode Decomposition and Manifold Interpolation0 aDataDriven Surrogate Modeling Approach for TimeDependent Incompr1 aHess, Martin, W.1 aQuaini, Annalisa1 aRozza, Gianluigi uhttps://math.sissa.it/publication/data-driven-surrogate-modeling-approach-time-dependent-incompressible-navier-stokes00476nas a2200097 4500008004100000245010500041210006900146100002100215700002100236856012100257 2022 eng d00aModel Reduction Using Sparse Polynomial Interpolation for the Incompressible Navier-Stokes Equations0 aModel Reduction Using Sparse Polynomial Interpolation for the In1 aHess, Martin, W.1 aRozza, Gianluigi uhttps://math.sissa.it/publication/model-reduction-using-sparse-polynomial-interpolation-incompressible-navier-stokes01776nas a2200169 4500008004100000020002200041245009500063210006900158260005200227520110800279100001601387700002101403700002101424700002301445700001901468856011901487 2021 eng d a978-3-030-55874-100aDiscontinuous Galerkin Model Order Reduction of Geometrically Parametrized Stokes Equation0 aDiscontinuous Galerkin Model Order Reduction of Geometrically Pa aChambSpringer International Publishingc2021//3 a

The present work focuses on the geometric parametrization and the reduced order modeling of the Stokes equation. We discuss the concept of a parametrized geometry and its application within a reduced order modeling technique. The full order model is based on the discontinuous Galerkin method with an interior penalty formulation. We introduce the broken Sobolev spaces as well as the weak formulation required for an affine parameter dependency. The operators are transformed from a fixed domain to a parameter dependent domain using the affine parameter dependency. The proper orthogonal decomposition is used to obtain the basis of functions of the reduced order model. By using the Galerkin projection the linear system is projected onto the reduced space. During this process, the offline-online decomposition is used to separate parameter dependent operations from parameter independent operations. Finally this technique is applied to an obstacle test problem.The numerical outcomes presented include experimental error analysis, eigenvalue decay and measurement of online simulation time.

1 aShah, Nirav1 aHess, Martin, W.1 aRozza, Gianluigi1 aVermolen, Fred, J.1 aVuik, Cornelis uhttps://math.sissa.it/publication/discontinuous-galerkin-model-order-reduction-geometrically-parametrized-stokes-002130nas a2200157 4500008004100000245011600041210006900157490000700226520151300233100002001746700002001766700002101786700002101807700002001828856012401848 2021 eng d00aEfficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method0 aEfficient computation of bifurcation diagrams with a deflated ap0 v473 a

The majority of the most common physical phenomena can be described using partial differential equations (PDEs). However, they are very often characterized by strong nonlinearities. Such features lead to the coexistence of multiple solutions studied by the bifurcation theory. Unfortunately, in practical scenarios, one has to exploit numerical methods to compute the solutions of systems of PDEs, even if the classical techniques are usually able to compute only a single solution for any value of a parameter when more branches exist. In this work, we implemented an elaborated deflated continuation method that relies on the spectral element method (SEM) and on the reduced basis (RB) one to efficiently compute bifurcation diagrams with more parameters and more bifurcation points. The deflated continuation method can be obtained combining the classical continuation method and the deflation one: the former is used to entirely track each known branch of the diagram, while the latter is exploited to discover the new ones. Finally, when more than one parameter is considered, the efficiency of the computation is ensured by the fact that the diagrams can be computed during the online phase while, during the offline one, one only has to compute one-dimensional diagrams. In this work, after a more detailed description of the method, we will show the results that can be obtained using it to compute a bifurcation diagram associated with a problem governed by the Navier-Stokes equations. 

1 aPintore, Moreno1 aPichi, Federico1 aHess, Martin, W.1 aRozza, Gianluigi1 aCanuto, Claudio uhttps://math.sissa.it/publication/efficient-computation-bifurcation-diagrams-deflated-approach-reduced-basis-spectral-000632nas a2200169 4500008004100000020001800041245010800059210006900167260003100236300001100267100002100278700002100299700002200320700001900342700001700361856008400378 2020 eng d a978311067149000aBasic ideas and tools for projection-based model reduction of parametric partial differential equations0 aBasic ideas and tools for projectionbased model reduction of par aBerlin, BostonbDe Gruyter a1 - 471 aRozza, Gianluigi1 aHess, Martin, W.1 aStabile, Giovanni1 aTezzele, Marco1 aBallarin, F. uhttps://www.degruyter.com/view/book/9783110671490/10.1515/9783110671490-001.xml02133nas a2200145 4500008004100000245011600041210006900157520162200226100002001848700002001868700002101888700002101909700002001930856003701950 2020 eng d00aEfficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method0 aEfficient computation of bifurcation diagrams with a deflated ap3 a

The majority of the most common physical phenomena can be described using partial differential equations (PDEs). However, they are very often characterized by strong nonlinearities. Such features lead to the coexistence of multiple solutions studied by the bifurcation theory. Unfortunately, in practical scenarios, one has to exploit numerical methods to compute the solutions of systems of PDEs, even if the classical techniques are usually able to compute only a single solution for any value of a parameter when more branches exist. In this work we implemented an elaborated deflated continuation method, that relies on the spectral element method (SEM) and on the reduced basis (RB) one, to efficiently compute bifurcation diagrams with more parameters and more bifurcation points. The deflated continuation method can be obtained combining the classical continuation method and the deflation one: the former is used to entirely track each known branch of the diagram, while the latter is exploited to discover the new ones. Finally, when more than one parameter is considered, the efficiency of the computation is ensured by the fact that the diagrams can be computed during the online phase while, during the offline one, one only has to compute one-dimensional diagrams. In this work, after a more detailed description of the method, we will show the results that can be obtained using it to compute a bifurcation diagram associated with a problem governed by the Navier-Stokes equations.

1 aPintore, Moreno1 aPichi, Federico1 aHess, Martin, W.1 aRozza, Gianluigi1 aCanuto, Claudio uhttps://arxiv.org/abs/1912.0608900540nas a2200145 4500008004100000245010800041210006900149653002100218653003300239100002100272700002100293700002200314700002100336856003700357 2020 eng d00aMicroROM: An Efficient and Accurate Reduced Order Method to Solve Many-Query Problems in Micro-Motility0 aMicroROM An Efficient and Accurate Reduced Order Method to Solve10aFOS: Mathematics10aNumerical Analysis (math.NA)1 aGiuliani, Nicola1 aHess, Martin, W.1 aDeSimone, Antonio1 aRozza, Gianluigi uhttps://arxiv.org/abs/2006.1383601531nas a2200145 4500008004100000245011100041210006900152300001200221490000700233520104500240100002101285700002101306700002101327856003701348 2020 eng d00aReduced Basis Model Order Reduction for Navier-Stokes equations in domains with walls of varying curvature0 aReduced Basis Model Order Reduction for NavierStokes equations i a119-1260 v343 a

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.

1 aHess, Martin, W.1 aQuaini, Annalisa1 aRozza, Gianluigi uhttps://arxiv.org/abs/1901.0370801653nas a2200145 4500008004100000245011300041210007100154300001200225490000700237520104600244100002101290700002101311700002101332856015401353 2020 eng d00aReduced basis model order reduction for Navier–Stokes equations in domains with walls of varying curvature0 aReduced basis model order reduction for Navier–Stokes equations  a119-1260 v343 a

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.

1 aHess, Martin, W.1 aQuaini, Annalisa1 aRozza, Gianluigi uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85085233294&doi=10.1080%2f10618562.2019.1645328&partnerID=40&md5=e2ed8f24c66376cdc8b5485aa400efb001338nas a2200145 4500008004100000245010000041210007100141300001200212490000800224520076800232100002101000700002101021700002101042856012901063 2020 eng d00aA spectral element reduced basis method for navier–stokes equations with geometric variations0 aspectral element reduced basis method for navier–stokes equation a561-5710 v1343 a

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.

1 aHess, Martin, W.1 aQuaini, Annalisa1 aRozza, Gianluigi uhttps://math.sissa.it/publication/spectral-element-reduced-basis-method-navier%E2%80%93stokes-equations-geometric-variations02128nas a2200169 4500008004100000245008400041210006900125300001200194490000800206520160300214100002101817700002101838700002101859700002101880700002001901856003701921 2019 eng d00aA Localized Reduced-Order Modeling Approach for PDEs with Bifurcating Solutions0 aLocalized ReducedOrder Modeling Approach for PDEs with Bifurcati a379-4030 v3513 a

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.

1 aHess, Martin, W.1 aAlla, Alessandro1 aQuaini, Annalisa1 aRozza, Gianluigi1 aGunzburger, Max uhttps://arxiv.org/abs/1807.0885102395nas a2200169 4500008004100000245008400041210006900125300001200194490000800206520175700214100002101971700002101992700002102013700002102034700002002055856015002075 2019 eng d00aA localized reduced-order modeling approach for PDEs with bifurcating solutions0 alocalized reducedorder modeling approach for PDEs with bifurcati a379-4030 v3513 a

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.

1 aHess, Martin, W.1 aAlla, Alessandro1 aQuaini, Annalisa1 aRozza, Gianluigi1 aGunzburger, Max uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85064313505&doi=10.1016%2fj.cma.2019.03.050&partnerID=40&md5=8b095034b9e539995facc7ce7bafa9e901397nas a2200193 4500008004100000245006200041210006000103260003800163490000800201520080600209100002101015700002101036700002501057700001801082700001601100700002801116700002201144856003701166 2019 eng d00aA Spectral Element Reduced Basis Method in Parametric CFD0 aSpectral Element Reduced Basis Method in Parametric CFD bSpringer International Publishing0 v1263 a

We consider the Navier-Stokes equations in a channel with varying Reynolds numbers. The model is discretized with high-order spectral element ansatz functions, resulting in 14 259 degrees of freedom. The steady-state snapshot solu- tions define a reduced order space, which allows to accurately evaluate the steady- state solutions for varying Reynolds number with a reduced order model within a fixed-point iteration. In particular, we compare different aspects of implementing the reduced order model with respect to the use of a spectral element discretization. It is shown, how a multilevel static condensation in the pressure and velocity boundary degrees of freedom can be combined with a reduced order modelling approach to enhance computational times in parametric many-query scenarios.

1 aHess, Martin, W.1 aRozza, Gianluigi1 aRadu, Florin, Adrian1 aKumar, Kundan1 aBerre, Inga1 aNordbotten, Jan, Martin1 aPop, Iuliu, Sorin uhttps://arxiv.org/abs/1712.0643201451nas a2200133 4500008004100000245006200041210006000103300001200163490000800175520093900183100002101122700002101143856015301164 2019 eng d00aA spectral element reduced basis method in parametric CFD0 aspectral element reduced basis method in parametric CFD a693-7010 v1263 a

We consider the Navier-Stokes equations in a channel with varying Reynolds numbers. The model is discretized with high-order spectral element ansatz functions, resulting in 14,259 degrees of freedom. The steady-state snapshot solutions define a reduced order space, which allows to accurately evaluate the steady-state solutions for varying Reynolds number with a reduced order model within a fixed-point iteration. In particular, we compare different aspects of implementing the reduced order model with respect to the use of a spectral element discretization. It is shown, how a multilevel static condensation (Karniadakis and Sherwin, Spectral/hp element methods for computational fluid dynamics, 2nd edn. Oxford University Press, Oxford, 2005) in the pressure and velocity boundary degrees of freedom can be combined with a reduced order modelling approach to enhance computational times in parametric many-query scenarios.

1 aHess, Martin, W.1 aRozza, Gianluigi uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85060005503&doi=10.1007%2f978-3-319-96415-7_64&partnerID=40&md5=d1a900db8ddb92cd818d797ec212a4c6