Abstract
In this chapter we introduce a combined parameter and model reduction methodology and present its application to the efficient numerical estimation of a pressure drop in a set of deformed carotids. The aim is to simulate a wide range of possible occlusions after the bifurcation of the carotid. A parametric description of the admissible deformations, based on radial basis functions interpolation, is introduced. Since the parameter space may be very large, the first step in the combined reduction technique is to look for active subspaces in order to reduce the parameter space dimension. Then, we rely on model order reduction methods over the lower dimensional parameter subspace, based on a POD-Galerkin approach, to further reduce the required computational effort and enhance computational efficiency.
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Notes
- 1.
In order to simplify the exposition we will report the FE formulation on the deformed domain Ω(μ). However, it should be noted that only the mesh Ω δ of the reference domain Ω is generated, and deformed meshes Ω δ(μ) are obtained through the mapping \(\mathcal {M}(\boldsymbol {\cdot }; {\boldsymbol {\mu }})\).
- 2.
The non-homogeneous right-hand side accounts for boundary conditions via a lifting.
- 3.
In this section we will omit the dependence on μ. It should be understood that f = f(μ), ρ = ρ(μ), etc.
References
Agoshkov, V., Quarteroni, A., Rozza, G.: A mathematical approach in the design of arterial bypass using unsteady Stokes equations. J. Sci. Comput. 28, 139–165 (2006)
Agoshkov, V., Quarteroni, A., Rozza, G.: Shape design in aorto-coronaric bypass anastomoses using perturbation theory. SIAM J. Numer. Anal. 44(1), 367–384 (2007)
Ali, S., Ballarin, F., Rozza, G.: Stabilized reduced basis methods for parametrized Stokes and Navier-Stokes equations. (2018, in preparation)
Ambrosi, D., Quarteroni, A., Rozza, G.: Modeling of Physiological Flows. MS&A – Modeling, Simulation and Applications, vol. 5. Springer, Berlin (2012)
Ballarin, F., Manzoni, A., Rozza, G., Salsa, S.: Shape optimization by Free-Form Deformation: existence results and numerical solution for Stokes flows. J. Sci. Comput. 60(3), 537–563 (2014)
Ballarin, F., Manzoni, A., Quarteroni, A., Rozza, G.: Supremizer stabilization of POD–Galerkin approximation of parametrized steady incompressible Navier–Stokes equations. Int. J. Numer. Methods Eng. 102(5), 1136–1161 (2015)
Ballarin, F., Faggiano, E., Ippolito, S., Manzoni, A., Quarteroni, A., Rozza, G., Scrofani, R.: Fast simulations of patient-specific haemodynamics of coronary artery bypass grafts based on a POD–Galerkin method and a vascular shape parametrization. J. Comput. Phys. 315, 609–628 (2016)
Ballarin, F., Sartori, A., Rozza, G.: RBniCS – reduced order modelling in fenics (2016). http://mathlab.sissa.it/rbnics
Ballarin, F., D’Amario, A., Perotto, S., Rozza, G.: A POD-selective inverse distance weighting method for fast parametrized shape morphing (2017, submitted). arXiv preprint arXiv:1710.09243
Ballarin, F., Faggiano, E., Manzoni, A., Quarteroni, A., Rozza, G., Ippolito, S., Antona, C., Scrofani, R.: Numerical modeling of hemodynamics scenarios of patient-specific coronary artery bypass grafts. Biomech. Model. Mechanobiol. 16(4), 1373–1399 (2017)
Barrault, M., Maday, Y., Nguyen, N.C., Patera, A.T.: An ‘empirical interpolation’ method: application to efficient reduced-basis discretization of partial differential equations. Comptes Rendus Mathematique 339(9), 667–672 (2004)
Benner, P., Ohlberger, M., Patera, A., Rozza, G., Urban, K. (eds.): Model Reduction of Parametrized Systems. MS&A – Modeling, Simulation and Applications, vol. 17. Springer, Berlin (2017)
Berkooz, G., Holmes, P., Lumley, J.: The proper orthogonal decomposition in the analysis of turbulent flows. Annu. Rev. Fluid Mech. 25(1), 539–575 (1993)
Box, G.E., Draper, N.R.: Empirical Model-Building and Response Surfaces, vol. 424. Wiley, New York (1987)
Brown, S.A.: Building supermodels: emerging patient avatars for use in precision and systems medicine. Front. Physiol. 6, 318 (2015)
Buhmann, M.D.: Radial Basis Functions: Theory and Implementations, vol. 12. Cambridge University Press, Cambridge (2003)
Caiazzo, A., Iliescu, T., John, V., Schyschlowa, S.: A numerical investigation of velocity-pressure reduced order models for incompressible flows. J. Comput. Phys. 259, 598–616 (2014)
Carlberg, K., Farhat, C., Cortial, J., Amsallem, D.: The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows. J. Comput. Phys. 242, 623–647 (2013)
Chen, P., Quarteroni, A.: Weighted reduced basis method for stochastic optimal control problems with elliptic PDE constraint. SIAM/ASA J. Uncertain. Quantif. 2(1), 364–396 (2014)
Chen, P., Quarteroni, A., Rozza, G.: Comparison between reduced basis and stochastic collocation methods for elliptic problems. J. Sci. Comput. 59(1), 187–216 (2014)
Chinesta, F., Keunings, R., Leygue, A.: The Proper Generalized Decomposition for Advanced Numerical Simulations: A Primer. Springer Science & Business Media, Berlin (2013)
Constantine, P.G.: Active Subspaces: Emerging Ideas for Dimension Reduction in Parameter Studies, vol. 2. SIAM, Philadelphia (2015)
Constantine, P., Gleich, D.: Computing active subspaces with Monte Carlo. arXiv preprint arXiv:1408.0545 (2015)
Constantine, P.G., Emory, M., Larsson, J., Iaccarino, G.: Exploiting active subspaces to quantify uncertainty in the numerical simulation of the HyShot II scramjet. J. Comput. Phys. 302, 1–20 (2015)
Constantine, P.G., Eftekhari, A., Ward, R.: A near-stationary subspace for ridge approximation (2016). arXiv preprint arXiv:1606.01929
Constantine, P., Howard, R., Glaws, A., Grey, Z., Diaz, P., Fletcher, L.: Python active-subspaces utility library. J. Open Source Softw. 1(5) (2016)
Cook, R.D.: Regression Graphics: Ideas for Studying Regressions Through Graphics, vol. 482. Wiley, New York (2009)
Cueto, E., Chinesta, F.: Real time simulation for computational surgery: a review. Adv. Model. Simul. Eng. Sci. 1(1), 11:1–11:18 (2014)
Devore, J.L.: Probability and Statistics for Engineering and the Sciences. Cengage Learning, Boston (2015)
Doorly, D., Sherwin, S.: Geometry and flow. In: Formaggia, L., Quarteroni, A., Veneziani, A. (eds.) Cardiovascular Mathematics. MS&A – Modeling, Simulation and Applications, vol. 1. Springer Italia, Milano (2009)
Dryden, I., Mardia, K.: Statistical Analysis of Shape. Wiley, New York (1998)
Duchon, J.: Splines minimizing rotation-invariant semi-norms in Sobolev spaces. In: Constructive Theory of Functions of Several Variables, pp. 85–100. Springer, Berlin (1977)
Forti, D., Rozza, G.: Efficient geometrical parametrisation techniques of interfaces for reduced-order modelling: application to fluid–structure interaction coupling problems. Int. J. Comput. Fluid Dyn. 28(3–4), 158–169 (2014)
Frey, P., George, P.: Mesh generation. Application to finite elements. Hermes Science Publishing, Paris, Oxford (2000)
González, D., Cueto, E., Chinesta, F.: Computational patient avatars for surgery planning. Ann. Biomed. Eng. 44(1), 35–45 (2016)
Guibert, R., Mcleod, K., Caiazzo, A., Mansi, T., Fernández, M.A., Sermesant, M., Pennec, X., Vignon-Clementel, I.E., Boudjemline, Y., Gerbeau, J.F.: Group-wise construction of reduced models for understanding and characterization of pulmonary blood flows from medical images. Med. Image Anal. 18(1), 63–82 (2014)
Gunzburger, M.D.: Perspectives in Flow Control and Optimization, vol. 5. SIAM, Philadelphia (2003)
Hesthaven, J.S., Rozza, G., Stamm, B.: Certified Reduced Basis Methods for Parametrized Partial Differential Equations. Springer Briefs in Mathematics. Springer, Berlin (2015)
Hokanson, J.M., Constantine, P.G.: Data-driven polynomial ridge approximation using variable projection (2017). arXiv preprint arXiv:1702.05859
Hu, X., Parks, G.T., Chen, X., Seshadri, P.: Discovering a one-dimensional active subspace to quantify multidisciplinary uncertainty in satellite system design. Adv. Space Res. 57(5), 1268–1279 (2016)
INRIA 3D Meshes Research Database. Available at: https://www.rocq.inria.fr/gamma/gamma/download/download.php
Jefferson, J.L., Gilbert, J.M., Constantine, P.G., Maxwell, R.M.: Reprint of: Active subspaces for sensitivity analysis and dimension reduction of an integrated hydrologic model. Comput. Geosci. 90, 78–89 (2016)
Kaipio, J., Somersalo, E.: Statistical and Computational Inverse Problems, vol. 160. Springer Science & Business Media, Berlin (2006)
Keiper, S.: Analysis of generalized ridge functions in high dimensions. In: 2015 International Conference on Sampling Theory and Applications (SampTA), pp. 259–263. IEEE, New York (2015)
Lassila, T., Rozza, G.: Parametric free-form shape design with PDE models and reduced basis method. Comput. Methods Appl. Mech. Eng. 199(23–24), 1583–1592 (2010)
Logg, A., Mardal, K.A., Wells, G.N.: Automated Solution of Differential Equations by the Finite Element Method. Springer, Berlin (2012)
Lukaczyk, T.W., Constantine, P., Palacios, F., Alonso, J.J.: Active subspaces for shape optimization. In: 10th AIAA Multidisciplinary Design Optimization Conference, p. 1171 (2014)
Manzoni, A., Quarteroni, A., Rozza, G.: Model reduction techniques for fast blood flow simulation in parametrized geometries. Int. J. Numer. Methods Biomed. Eng. 28(6–7), 604–625 (2012)
Marsden, A.L.: Optimization in cardiovascular modeling. Annu. Rev. Fluid Mech. 46(1), 519–546 (2014)
McLeod, K., Caiazzo, A., Fernández, M., Mansi, T., Vignon-Clementel, I., Sermesant, M., Pennec, X., Boudjemline, Y., Gerbeau, J.F.: Atlas-based reduced models of blood flows for fast patient-specific simulations. In: Camara, O., Pop, M., Rhode, K., Sermesant, M., Smith, N., Young, A. (eds.) Statistical Atlases and Computational Models of the Heart. Lecture Notes in Computer Science, vol. 6364, pp. 95–104. Springer, Berlin/Heidelberg (2010)
Metropolis, N., Ulam, S.: The monte carlo method. J. Am. Stat. Assoc. 44(247), 335–341 (1949)
Morris, M.: Factorial sampling plans for preliminary computational experiments. Technometrics 33(2), 161–174 (1991)
Morris, A., Allen, C., Rendall, T.: CFD-based optimization of aerofoils using radial basis functions for domain element parameterization and mesh deformation. Int. J. Numer. Methods Fluids 58(8), 827–860 (2008)
Pinkus, A.: Ridge Functions, vol. 205. Cambridge University Press, Cambridge (2015)
PyGeM: Python Geometrical Morphing. Available at https://github.com/mathLab/PyGeM
Quarteroni, A., Rozza, G.: Reduced Order Methods for Modeling and Computational Reduction. MS&A – Modeling, Simulation and Applications, vol. 9. Springer, Berlin (2014)
Ravindran, S.: A reduced-order approach for optimal control of fluids using proper orthogonal decomposition. Int. J. Numer. Meth. Fluids 34, 425–448 (2000)
Rozza, G., Veroy, K.: On the stability of the reduced basis method for Stokes equations in parametrized domains. Comput. Methods Appl. Mech. Eng. 196(7), 1244–1260 (2007)
Sederberg, T., Parry, S.: Free-Form Deformation of solid geometric models. In: Proceedings of SIGGRAPH - Special Interest Group on GRAPHics and Interactive Techniques, pp. 151–159. SIGGRAPH (1986)
Shepard, D.: A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings-1968 ACM National Conference, pp. 517–524. ACM, New York (1968)
Stabile, G., Rozza, G.: Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equations. Comput. Fluids (2018). https://doi.org/10.1016/j.compfluid.2018.01.035
Tezzele, M., Salmoiraghi, F., Mola, A., Rozza, G.: Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems. Adv. Model. Simul. Eng. Sci. (2018, in press). Preprint, arXiv:1709.03298
Torlo, D., Ballarin, F., Rozza, G.: Stabilized reduced basis methods for advection dominated partial differential equations with random inputs (2017, submitted)
Venturi, L., Ballarin, F., Rozza, G.: Weighted POD–Galerkin methods for parametrized partial differential equations in uncertainty quantification problems (2017, submitted)
Wang, V.Y., Hoogendoorn, C., Frangi, A.F., Cowan, B.R., Hunter, P.J., Young, A.A., Nash, M.P.: Automated personalised human left ventricular FE models to investigate heart failure mechanics. In: Proceedings of the Third International Conference on Statistical Atlases and Computational Models of the Heart: Imaging and Modelling Challenges, STACOM’12, pp. 307–316. Springer, Berlin, Heidelberg (2013)
Witteveen, J., Bijl, H.: Explicit mesh deformation using Inverse Distance Weighting interpolation. In: 19th AIAA Computational Fluid Dynamics. AIAA, Washington (2009)
Zarins, C.K., Giddens, D.P., Bharadvaj, B., Sottiurai, V.S., Mabon, R.F., Glagov, S.: Carotid bifurcation atherosclerosis: quantitative correlation of plaque localization with flow velocity profiles and wall shear stress. Circ. Res. 53(4), 502–514 (1983)
Acknowledgements
This work was partially supported by the INDAM-GNCS 2017 project “Advanced numerical methods combined with computational reduction techniques for parameterised PDEs and applications”, and by European Union Funding for Research and Innovation—Horizon 2020 Program—in the framework of European Research Council Executive Agency: H2020 ERC CoG 2015 AROMA-CFD project 681447 “Advanced Reduced Order Methods with Applications in Computational Fluid Dynamics” P.I. Gianluigi Rozza.
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Tezzele, M., Ballarin, F., Rozza, G. (2018). Combined Parameter and Model Reduction of Cardiovascular Problems by Means of Active Subspaces and POD-Galerkin Methods. In: Boffi, D., Pavarino, L., Rozza, G., Scacchi, S., Vergara, C. (eds) Mathematical and Numerical Modeling of the Cardiovascular System and Applications. SEMA SIMAI Springer Series, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-319-96649-6_8
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