We propose a new model reduction framework for problems that exhibit transport phenomena. As in the moving finite element method (MFEM), our method employs time-dependent transformation operators and, especially, generalizes MFEM to arbitrary basis functions. The new framework is suitable to obtain a low-dimensional approximation with small errors even in situations where classical model order reduction techniques require much higher dimensions for a similar approximation quality. Analogously to the MFEM framework, the reduced model is designed to minimize the residual, which is also the basis for an a posteriori error bound. Moreover, since the dependence of the transformation operators on the reduced state is nonlinear, the resulting reduced order model is obtained by projecting the original evolution equation onto a nonlinear manifold. Furthermore, for a special case, we show a connection between our approach and the method of freezing, which is also known as symmetry reduction. Besides the construction of the reduced order model, we also analyze the problem of finding optimal basis functions based on given data of the full order solution. Especially, we show that the corresponding minimization problem has a solution and reduces to the proper orthogonal decomposition of transformed data in a special case. Finally, we demonstrate the effectiveness of our method with several analytical and numerical examples.
Mots-clés : Transport dominated phenomena, model order reduction, error bound, nonlinear Galerkin, residual minimization
@article{M2AN_2020__54_6_2011_0, author = {Black, Felix and Schulze, Philipp and Unger, Benjamin}, title = {Projection-based model reduction with dynamically transformed modes}, journal = {ESAIM: Mathematical Modelling and Numerical Analysis }, pages = {2011--2043}, publisher = {EDP-Sciences}, volume = {54}, number = {6}, year = {2020}, doi = {10.1051/m2an/2020046}, mrnumber = {4160330}, zbl = {1470.35276}, language = {en}, url = {http://www.numdam.org/articles/10.1051/m2an/2020046/} }
TY - JOUR AU - Black, Felix AU - Schulze, Philipp AU - Unger, Benjamin TI - Projection-based model reduction with dynamically transformed modes JO - ESAIM: Mathematical Modelling and Numerical Analysis PY - 2020 SP - 2011 EP - 2043 VL - 54 IS - 6 PB - EDP-Sciences UR - http://www.numdam.org/articles/10.1051/m2an/2020046/ DO - 10.1051/m2an/2020046 LA - en ID - M2AN_2020__54_6_2011_0 ER -
%0 Journal Article %A Black, Felix %A Schulze, Philipp %A Unger, Benjamin %T Projection-based model reduction with dynamically transformed modes %J ESAIM: Mathematical Modelling and Numerical Analysis %D 2020 %P 2011-2043 %V 54 %N 6 %I EDP-Sciences %U http://www.numdam.org/articles/10.1051/m2an/2020046/ %R 10.1051/m2an/2020046 %G en %F M2AN_2020__54_6_2011_0
Black, Felix; Schulze, Philipp; Unger, Benjamin. Projection-based model reduction with dynamically transformed modes. ESAIM: Mathematical Modelling and Numerical Analysis , Tome 54 (2020) no. 6, pp. 2011-2043. doi : 10.1051/m2an/2020046. http://www.numdam.org/articles/10.1051/m2an/2020046/
Approximation of Large-Scale Dynamical Systems. Advances in Design and Control. SIAM, Philadelphia, PA, USA (2005). | MR | Zbl
,Model reduction by nice selections for linear switched systems. IEEE Trans. Autom. Control 61 (2016) 3422–3437. | DOI | MR | Zbl
, , and ,An “empirical interpolation” method: application to efficient reduced-basis discretization of partial differential equations. C. R. Math. Acad. Sci. Paris 339 (2004) 667–672. | DOI | MR | Zbl
, , and ,Model order reduction for linear and nonlinear systems: a system-theoretic perspective. Arch. Comput. Methods Eng. 21 (2014) 331–358. | DOI | MR | Zbl
, and ,A survey of projection-based model reduction methods for parametric dynamical systems. SIAM Rev. 57 (2015) 483–531. | DOI | MR | Zbl
, and ,Model Reduction and Approximation. Advances in Design and Control. SIAM, Philadelphia, PA, USA (2017). | MR | Zbl
, , and ,Freezing solutions of equivariant evolution equations. SIAM J. Appl. Dyn. Syst. 3 (2004) 85–116. | DOI | MR | Zbl
and ,Nonlinear oscillations in a distributed network. Q. Appl. Math. 24 (1967) 289–301. | DOI | Zbl
,Functional Analysis, Sobolev Spaces and Partial Differential Equations. Universitext. Springer, New York, NY, USA (2011). | DOI | MR | Zbl
,Model order reduction for hyperbolic problems: a new framework. Preprint (2017). | HAL
, , and ,Model order reduction for problems with large convection effects. In: Computational Methods in Applied Sciences, Springer, Cham, Switzerland (2019) 131–150. | DOI | MR | Zbl
, and ,Adaptive -refinement for reduced-order models. Int. J. Numer. Methods Eng. 102 (2015) 1192–1210. | DOI | MR | Zbl
,Galerkin v. least-squares Petrov–Galerkin projection in nonlinear model reduction. J. Comput. Phys. 330 (2017) 693–734. | DOI | MR | Zbl
, and ,Nonlinear model reduction via discrete empirical interpolation. SIAM J. Sci. Comput. 32 (2010) 2737–2764. | DOI | MR | Zbl
and ,Differential-difference equations and nonlinear initial-boundary value problems for linear hyperbolic partial differential equations. J. Math. Anal. App. 24 (1968) 372–387. | DOI | MR | Zbl
and ,Model reduction of parametrized evolution problems using the reduced basis method with adaptive time partitioning. In: International Conference on Adaptive Modeling and Simulation (2011) 156–167.
, and ,One-Parameter Semigroups for Linear Evolution Equations. Graduate Texts in Mathematics. Springer, New York, NY, USA (2000). | MR | Zbl
and ,Online adaptive basis refinement and compression for reduced-order models via vector-space sieving. Preprint (2019). | arXiv | MR
and ,Generation of finite difference formulas on arbitrarily spaced grids. Math. Comput. 51 (1988) 699–706. | DOI | MR | Zbl
,From time-domain data to low-dimensional structured models. Preprint arXiv:1902.05112 (2019).
, and ,The moving finite element method: applications to general partial differential equations with multiple large gradients. J. Comput. Phys. 40 (1981) 202–249. | DOI | MR | Zbl
, and ,Approximated Lax pairs for the reduced order integration of nonlinear evolution equations. J. Comput. Phys. 265 (2014) 246–269. | DOI | MR | Zbl
and ,Model reduction, centering, and the Karhunen-Loeve expansion. In: Vol. 2 of Proceedings of the 37th IEEE Conference on Decision and Control. Tampa, FL, USA (1998) 2071–2076.
, and ,On Nemytskij operators in -spaces of abstract functions. Math. Nachr. 155 (1992) 127–140. | DOI | MR | Zbl
, and ,Balanced truncation for linear switched systems. Adv. Comput. Math. 44 (2018) 1845–1886. | DOI | MR | Zbl
, , and ,Decay of the Kolmogorov -width for wave problems. Appl. Math. Lett. 96 (2019) 216–222. | DOI | MR | Zbl
and ,Reduced-basis approximations and a posteriori error estimation for parabolic partial differential equations. Ph.D. thesis. Massachusetts Institute of Technology (2005).
,QLMOR: a projection-based nonlinear model order reduction approach using quadratic-linear representation of nonlinear systems. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 30 (2011) 1307–1320. | DOI
,Proper orthogonal decomposition for linear-quadratic optimal control, chapter 1, edited by , , and . In: Model Reduction and Approximation. SIAM, Philadelphia, PA, USA (2017) 3–63. | MR
and ,Convergence rates of the POD – greedy method. ESAIM: M2AN 47 (2013) 859–873. | DOI | Numdam | MR | Zbl
,Reduced basis method for finite volume approximations of parametrized linear evolution equations. ESAIM: M2AN 42 (2008) 277–302. | DOI | Numdam | MR | Zbl
and ,A deep learning framework for model reduction of dynamical systems. In: IEEE Conference on Control Technology and Applications (CCTA). Kohala Coast, HI, USA (2017) 1917–1922. | DOI
and ,Certified Reduced Basis Methods for Parametrized Partial Differential Equations. Springer Briefs in Mathematics. Springer, Cham, Switzerland (2016). | MR | Zbl
, and ,Theoretical background: aeroacoustics, In: Large-Eddy Simulation for Acoustics, edited by , and . Cambridge University Press, Cambridge, UK (2007) 24–88. | DOI
and ,Turbulence, Coherent Structures, Dynamical Systems and Symmetry, 2nd edition. Cambridge Monographs on Mechanics. Cambridge University Press, New York, NY, USA (2012). | DOI | MR | Zbl
, , and ,Advection modes by optimal mass transfer. Phys. Rev. E 89 (2014) 022923. | DOI
and ,Certified reduced-basis solutions of viscous Burgers equation parametrized by initial and boundary values. ESAIM: M2AN 47 (2013) 317–348. | DOI | Numdam | MR | Zbl
, and ,Projection-based reduced order models for a cut finite element method in parametrized domains. Preprint (2019). | arXiv | MR
, and ,Nonlinear model reduction by deep autoencoder of noise response data. In: 55th IEEE Conference on Decision and Control (CDC). Las Vegas, USA (2016) 5750–5755.
,Über die beste Annäherung von Funktionen einer gegebenen Funktionenklasse. Ann. Math. 37 (1936) 107–110. | DOI | JFM | MR | Zbl
,Nonlinear model order reduction via lifting transformations and proper orthogonal decomposition. AIAA J. 57 (2019) 2297–2307. | DOI
and ,Differential-Algebraic Equations. Analysis and Numerical Solution. European Mathematical Society, Zürich, Switzerland (2006). | DOI | MR | Zbl
and ,Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders. J. Comput. Phys. 404 (2019) 108973. | DOI | MR | Zbl
and ,Stability and forced oscillations. J. Math. Anal. Appl. 55 (1976) 686–698. | DOI | MR | Zbl
,Global a priori convergence theory for reduced-basis approximations of single-parameter symmetric coercive elliptic partial differential equations. C. R. Math. Acad. Sci. Paris 335 (2002) 289–294. | DOI | MR | Zbl
, and ,A priori convergence theory for reduced-basis approximations of single-parameter elliptic partial differential equations. J. Sci. Comput. 17 (2002) 437–446. | DOI | MR | Zbl
, and ,Dimensionality reduction and reduced order modeling for traveling wave physics. Preprint (2019). | arXiv | MR
, , , and ,Moving finite elements. I. SIAM J. Numer. Anal. 18 (1981) 1019–1032. | DOI | MR | Zbl
and ,Lagrangian basis method for dimensionality reduction of convection dominated nonlinear flows. Preprint (2019). | arXiv
and ,Model order reduction for stochastic dynamical systems with continuous symmetries. SIAM J. Sci. Comput. 40 (2018) A1669–A1695. | DOI | MR | Zbl
and ,Transported snapshot model order reduction approach for parametric, steady-state fluid flows containing parameter-dependent shocks. Int. J. Numer. Methods Eng. 117 (2019) 1234–1262. | DOI | MR | Zbl
and ,Overcoming slowly decaying Kolmogorov n-width by transport maps: application to model order reduction of fluid dynamics and fluid–structure interaction problems. Preprint (2019). | arXiv
, , and ,Nonlinear reduced basis approximation of parameterized evolution equations via the method of freezing. C. R. Math. Acad. Sci. Paris 351 (2013) 901–906. | DOI | MR | Zbl
and ,Semigroups of Linear Operators and Applications to Partial Differential Equations. Applied Mathematical Sciences. Springer, New York, NY, USA (1983). | DOI | MR | Zbl
,Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling. Preprint (2018). | arXiv | MR
,-Widths in Approximation Theory. Ergebnisse der Mathematik und ihrer Grenzgebiete. Springer, Heidelberg, Germany (1985). | MR | Zbl
,-optimal model approximation by input/output-delay structured reduced order models. Syst. Control Lett. 117 (2018) 60–67. | DOI | MR | Zbl
, and ,Reduced Order Methods for Modeling and Computational Reduction. In Vol. 9 of MS&A – Simulation and Applications. Springer, Cham, Switzerland (2014). | MR | Zbl
and ,Reduced Basis Methods for Partial Differential Equations: An Introduction. UNITEXT. Springer, Cham, Switzerland (2016). | MR | Zbl
, and ,Model reduction for convective problems: formulation and application. IFAC-PapersOnLine 51 (2018) 186–189. | DOI
,The shifted proper orthogonal decomposition: a mode decomposition for multiple transport phenomena. SIAM J. Sci. Comput. 40 (2018) A1322–A1344. | DOI | MR | Zbl
, , and ,Transport reversal for model reduction of hyperbolic partial differential equations. SIAM/ASA J. Uncertain. Quantif. 6 (2018) 118–150. | DOI | MR | Zbl
, and ,Manifold approximations via transported subspaces: model reduction for transport-dominated problems. Preprint (2020). | arXiv | MR
, and ,Reconstruction equations and the Karhunen-Loève expansion for systems with symmetry. Phys. D 142 (2000) 1–19. | DOI | MR | Zbl
and ,Reduction and reconstruction for self-similar dynamical systems. Nonlinearity 16 (2003) 1257–1275. | DOI | MR | Zbl
, , and ,Model reduction for hybrid systems with state-dependent jumps. IFAC-PapersOnLine 49 (2016) 850–855. | DOI
and ,Model reduction of neutral linear and nonlinear time-invariant time-delay systems with discrete and distributed delays. IEEE Trans. Automat. Contr. 61 (2016) 1438–1451. | DOI | MR | Zbl
and ,Data-driven interpolation of dynamical systems with delay. Syst. Control Lett. 97 (2016) 125–131. | DOI | MR | Zbl
and ,Model reduction for linear systems with low-rank switching. SIAM J. Control Optim. 56 (2018) 4365–4384. | DOI | MR | Zbl
and ,Data-driven structured realization. Linear Algebra Appl. 537 (2018) 250–286. | DOI | MR | Zbl
, , and ,Model reduction for a pulsed detonation combuster via shifted proper orthogonal decomposition, In: Active Flow and Combustion Control 2018, edited by . Springer, Cham, Switzerland (2019) 271–286. | DOI
, and ,A characteristic dynamic mode decomposition. Theor. Comput. Fluid Dyn. 33 (2019) 281–305. | DOI | MR
and ,Angular synchronization by eigenvectors and semidefinite programming. Appl. Comput. Harmon. Anal. 30 (2011) 20–36. | DOI | MR | Zbl
,Noisy dynamic simulations in the presence of symmetry: data alignment and model reduction. Comput. Math. Appl. 65 (2013) 1535–1557. | DOI | MR | Zbl
, and ,A registration method for model order reduction: data compression and geometry reduction. Preprint (2019). | arXiv | MR
,Reduced basis techniques for nonlinear conservation laws. ESAIM:M2AN 49 (2015) 787–814. | DOI | Numdam | MR | Zbl
, and ,Impact of discretization techniques on nonlinear model reduction and analysis of the structure of the POD basis. Master’s thesis, Virginia Polytechnic and State University, Blacksburg, Virginia, USA (2013).
,Kolmogorov -widths for linear dynamical systems. Adv. Comput. Math. 45 (2019) 2273–2286. | DOI | MR | Zbl
and ,An improved error bound for reduced basis approximation of linear parabolic problems. Math. Comput. 83 (2014) 1599–1615. | DOI | MR | Zbl
and ,Optimal control of a phase-field model using proper orthogonal decomposition. ZAMM Z. Angew. Math. Mech. 81 (2001) 83–97. | DOI | MR | Zbl
,Nonlinear Functional Analysis and its Applications IIa: Linear Monotone Operators. Springer, New York, NY, USA (1990). | MR | Zbl
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