@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and Manifold Interpolation}, year = {2022}, author = {Martin W. Hess and Annalisa Quaini and Gianluigi Rozza} } @unpublished {2022, title = {Model Reduction Using Sparse Polynomial Interpolation for the Incompressible Navier-Stokes Equations}, year = {2022}, author = {Martin W. Hess and Gianluigi Rozza} } @conference {2021, title = {Discontinuous Galerkin Model Order Reduction of Geometrically Parametrized Stokes Equation}, booktitle = {Numerical Mathematics and Advanced Applications ENUMATH 2019}, year = {2021}, month = {2021//}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

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.

}, isbn = {978-3-030-55874-1}, author = {Nirav Shah and Martin W. Hess and Gianluigi Rozza}, editor = {Vermolen, Fred J. and Vuik, Cornelis} } @article {2021, title = {Efficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method}, journal = {Advances in Computational Mathematics}, volume = {47}, year = {2021}, abstract = {

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.\ 

}, doi = {10.1007/s10444-020-09827-6}, author = {Moreno Pintore and Federico Pichi and Martin W. Hess and Gianluigi Rozza and Claudio Canuto} } @inbook {RozzaHessStabileTezzeleBallarin2020, title = {Basic ideas and tools for projection-based model reduction of parametric partial differential equations}, booktitle = {Model Order Reduction, Volume 2 Snapshot-Based Methods and Algorithms}, year = {2020}, pages = {1 - 47}, publisher = {De Gruyter}, organization = {De Gruyter}, address = {Berlin, Boston}, isbn = {9783110671490}, doi = {https://doi.org/10.1515/9783110671490-001}, url = {https://www.degruyter.com/view/book/9783110671490/10.1515/9783110671490-001.xml}, author = {Gianluigi Rozza and Martin W. Hess and Giovanni Stabile and Marco Tezzele and F. Ballarin} } @article {13850, title = {Efficient computation of bifurcation diagrams with a deflated approach to reduced basis spectral element method}, journal = {Advances in Computational Mathematics}, year = {2020}, abstract = {

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.

}, url = {https://arxiv.org/abs/1912.06089}, author = {Moreno Pintore and Federico Pichi and Martin W. Hess and Gianluigi Rozza and Claudio Canuto} } @booklet {2020, title = {MicroROM: An Efficient and Accurate Reduced Order Method to Solve Many-Query Problems in Micro-Motility}, year = {2020}, keywords = {FOS: Mathematics, Numerical Analysis (math.NA)}, doi = {10.48550/ARXIV.2006.13836}, url = {https://arxiv.org/abs/2006.13836}, author = {Nicola Giuliani and Martin W. Hess and Antonio DeSimone 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 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 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 {2019, title = {A spectral element reduced basis method in parametric CFD}, journal = {Lecture Notes in Computational Science and Engineering}, volume = {126}, year = {2019}, pages = {693-701}, abstract = {

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.

}, doi = {10.1007/978-3-319-96415-7_64}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060005503\&doi=10.1007\%2f978-3-319-96415-7_64\&partnerID=40\&md5=d1a900db8ddb92cd818d797ec212a4c6}, author = {Martin W. Hess and Gianluigi Rozza} } @inbook {2019, title = {A Spectral Element Reduced Basis Method in Parametric CFD}, booktitle = {Numerical Mathematics and Advanced Applications - ENUMATH 2017}, volume = {126}, year = {2019}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {A Spectral Element Reduced Basis Method in Parametric CFD}, abstract = {

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.

}, doi = {10.1007/978-3-319-96415-7_64 pages = 693{\textendash}701}, url = {https://arxiv.org/abs/1712.06432}, author = {Martin W. Hess and Gianluigi Rozza}, editor = {Radu, Florin Adrian and Kumar, Kundan and Berre, Inga and Nordbotten, Jan Martin and Pop, Iuliu Sorin} }