In this paper, we extend the reduced-basis methods and associated a posteriori error estimators developed earlier for elliptic partial differential equations to parabolic problems with affine parameter dependence. The essential new ingredient is the presence of time in the formulation and solution of the problem - we shall “simply” treat time as an additional, albeit special, parameter. First, we introduce the reduced-basis recipe - Galerkin projection onto a space spanned by solutions of the governing partial differential equation at selected points in parameter-time space - and develop a new greedy adaptive procedure to “optimally” construct the parameter-time sample set. Second, we propose error estimation and adjoint procedures that provide rigorous and sharp bounds for the error in specific outputs of interest: the estimates serve a priori to construct our samples, and a posteriori to confirm fidelity. Third, based on the assumption of affine parameter dependence, we develop offline-online computational procedures: in the offline stage, we generate the reduced-basis space; in the online stage, given a new parameter value, we calculate the reduced-basis output and associated error bound. The operation count for the online stage depends only on (typically small) and the parametric complexity of the problem; the method is thus ideally suited for repeated, rapid, reliable evaluation of input-output relationships in the many-query or real-time contexts.
Mots-clés : parabolic partial differential equations, diffusion equation, parameter-dependent systems, reduced-basis methods, output bounds, Galerkin approximation, a posteriori error estimation
@article{M2AN_2005__39_1_157_0, author = {Grepl, Martin A. and Patera, Anthony T.}, title = {A posteriori error bounds for reduced-basis approximations of parametrized parabolic partial differential equations}, journal = {ESAIM: Mod\'elisation math\'ematique et analyse num\'erique}, pages = {157--181}, publisher = {EDP-Sciences}, volume = {39}, number = {1}, year = {2005}, doi = {10.1051/m2an:2005006}, mrnumber = {2136204}, zbl = {1079.65096}, language = {en}, url = {http://archive.numdam.org/articles/10.1051/m2an:2005006/} }
TY - JOUR AU - Grepl, Martin A. AU - Patera, Anthony T. TI - A posteriori error bounds for reduced-basis approximations of parametrized parabolic partial differential equations JO - ESAIM: Modélisation mathématique et analyse numérique PY - 2005 SP - 157 EP - 181 VL - 39 IS - 1 PB - EDP-Sciences UR - http://archive.numdam.org/articles/10.1051/m2an:2005006/ DO - 10.1051/m2an:2005006 LA - en ID - M2AN_2005__39_1_157_0 ER -
%0 Journal Article %A Grepl, Martin A. %A Patera, Anthony T. %T A posteriori error bounds for reduced-basis approximations of parametrized parabolic partial differential equations %J ESAIM: Modélisation mathématique et analyse numérique %D 2005 %P 157-181 %V 39 %N 1 %I EDP-Sciences %U http://archive.numdam.org/articles/10.1051/m2an:2005006/ %R 10.1051/m2an:2005006 %G en %F M2AN_2005__39_1_157_0
Grepl, Martin A.; Patera, Anthony T. A posteriori error bounds for reduced-basis approximations of parametrized parabolic partial differential equations. ESAIM: Modélisation mathématique et analyse numérique, Tome 39 (2005) no. 1, pp. 157-181. doi : 10.1051/m2an:2005006. http://archive.numdam.org/articles/10.1051/m2an:2005006/
[1] Automatic choice of global shape functions in structural analysis. AIAA J. 16 (1978) 525-528.
, and ,[2] Proper orthogonal decomposition for reduced basis feedback controllers for parabolic equations. Math. Comput. Mod. 33 (2001) 1-19. | Zbl
and ,[3] Parametric families of reduced finite element models: Theory and applications. Mech. Syst. Signal Process. 10 (1996) 381-394.
,[4] Reduced-order models for nonlinear distributed process systems and their application in dynamic optimization. Indust. Engineering Chemistry Res. 43 (2004) 3353-3363.
, and ,[5] Estimation Techniques for Distributed Parameter Systems. Systems & Control: Foundations & Applications. Birkhäuser (1989). | MR | Zbl
and ,[6] An “empirical interpolation” method: Application to efficient reduced-basis discretization of partial differential equations. C. R. Acad. Sci. Paris, Sér. I. 339 (2004) 667-672. | Zbl
, , and ,[7] On the reduced basis method. Z. Angew. Math. Mech. 75 (1995) 543-549. | Zbl
and ,[8] Weighted a posteriori error control in finite element methods. In ENUMATH 95 Proc. World Sci. Publ., Singapore (1997).
and ,[9] Introduction to Linear Optimization. Athena Scientific (1997). | Zbl
and ,[10] Evaluation of proper orthogonal decomposition-based decomposition techniques applied to parameter-dependent nonturbulent flows. SIAM J. Sci. Comput. 21 (2000) 1419-1434. | Zbl
, and , , and , Eds., Control and Estimation of Distributed Parameter Systems, volume 126 of International Series of Numerical Mathematics. Birkhäuser (1998). |[12] Coordinating feedback and switching for control of spatially distributed processes. Comput. Chemical Engineering 28 (2004) 111-128.
and ,[13] On the error behavior of the reduced basis technique for nonlinear finite element approximations. Z. Angew. Math. Mech. 63 (1983) 21-28. | Zbl
and ,[14] Reduced-Basis Approximations for Time-Dependent Partial Differential Equations: Application to Optimal Control. Ph.D. Thesis, Massachusetts Institute of Technology (2005) (in progress).
, , and , Eds., Optimal Control of Partial Differential Equations, volume 133 of International Series of Numerical Mathematics. Birkhäuser (1998). |[16] A reduced basis method for control problems governed by PDEs, in Control and Estimation of Distributed Parameter Systems, W. Desch, F. Kappel, and K. Kunisch Eds., Birkhäuser (1998) 153-168. | Zbl
and ,[17] A reduced-order method for simulation and control of fluid flows. J. Comput. Phys. 143 (1998) 403-425. | Zbl
and ,[18] Reduced basis method for optimal control of unsteady viscous flows. Int. J. Comput. Fluid Dyn. 15 (2001) 97-113. | Zbl
and ,[19] A subspace approach to balanced truncation for model reduction of nonlinear control systems. Int. J. Robust Nonlinear Control 12 (2002) 519-535. | Zbl
, and ,[20] Estimation of the error in the reduced basis method solution of differential algebraic equation systems. SIAM J. Numer. Anal. 28 (1991) 512-528. | Zbl
,[21] Optimal Control of Systems Governed by Partial Differential Equations. Springer (1971). | MR | Zbl
,[22] Output bounds for reduced-basis approximations of symmetric positive definite eigenvalue problems. C. R. Acad. Sci. Paris, Sér. I 331 (2000) 153-158. | Zbl
, , , and ,[23] A blackbox reduced-basis output bound method for noncoercive linear problems, in Nonlinear Partial Differential Equations and Their Applications, Collège de France Seminar Volume XIV, D. Cioranescu and J.-L. Lions Eds., Elsevier Science B.V. (2002) 533-569. | Zbl
, and ,[24] Reduced-order modeling for hyperthermia: An extended balanced-realization-based approach. IEEE Transactions on Biomedical Engineering 45 (1998) 1154-1162.
, , , and ,[25] Exact temperature tracking for hyperthermia: A model-based approach. IEEE Trans. Control Systems Technology 8 (2000) 979-992.
, and ,[26] Principal component analysis in linear systems: controllability, observability, and model reduction. IEEE Trans. Automat. Control 26 (1981) 17-32. | Zbl
,[27] Modal representation of geometrically nonlinear behaviour by the finite element method. Comput. Structures 10 (1979) 683-688. | Zbl
,[28] Reduced basis technique for nonlinear analysis of structures. AIAA J. 18 (1980) 455-462.
and ,[29] Reduced-basis techniques for rapid reliable optimization of systems described by affinely parametrized coercive elliptic partial differential equations. Optim. Engineering (2005) (submitted).
and ,[30] Optimal control of rapid thermal processing systems by empirical reduction of modes. Ind. Eng. Chem. Res. 38 (1999) 3964-3975.
, and ,[31] The reduced basis method for incompressible viscous flow calculations. SIAM J. Sci. Stat. Comput. 10 (1989) 777-786. | Zbl
,[32] Estimation of the error in the reduced basis method solution of nonlinear equations. Math. Comp. 45 (1985) 487-496. | Zbl
,[33] The reduced basis method for initial value problems. SIAM J. Numer. Anal. 24 (1987) 1277-1287. | Zbl
and ,[34] Reliable real-time solution of parametrized partial differential equations: Reduced-basis output bound methods. J. Fluids Engineering 124 (2002) 70-80.
, , , , and ,[35] Numerical Approximation of Partial Differential Equations. Springer, 2nd edition (1997). | MR | Zbl
and ,[36] A reduced-order approach for optimal control of fluids using proper orthogonal decomposition. Int. J. Numer. Meth. Fluids 34 (2000) 425-448. | Zbl
,[37] On the theory and error estimation of the reduced basis method for multi-parameter problems. Nonlinear Anal. 21 (1993) 849-858. | Zbl
,[38] Reduced-basis output bound methods for parabolic problems. IMA J. Appl. Math. (2005) (submitted). | MR | Zbl
, and ,[39] Reduced-Basis Output Bound Methods for Parametrized Partial Differential Equations. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA (2002).
,[40] Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Amer. A 4 (1987) 519-524.
and ,[41] Certified real-time solution of the parametrized steady incompressible navier-stokes equations; Rigorous reduced-basis a posteriori error bounds. Internat. J. Numer. Methods Fluids (2005) (to appear). | MR | Zbl
and ,[42] Reduced-basis approximation of the viscous Burgers equation: Rigorous a posteriori error bounds. C. R. Acad. Sci. Paris, Sér. I 337 (2003) 619-624. | Zbl
, and ,[43] A posteriori error bounds for reduced-basis approximation of parametrized noncoercive and nonlinear elliptic partial differential equations (AIAA Paper 2003-3847), in Proceedings of the 16th AIAA Computational Fluid Dynamics Conference (June 2003).
, , and ,[44] A Posteriori error estimation for reduced-basis approximation of parametrized elliptic coercive partial differential equations: “Convex inverse” bound conditioners. ESAIM: COCV 8 (2002) 1007-1028. Special Volume: A tribute to J.-L. Lions. | Numdam | Zbl
, and ,[45] Balanced model reduction via the proper orthogonal decomposition, in 15th AIAA Computational Fluid Dynamics Conference, AIAA (June 2001).
and ,Cité par Sources :