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An Efficient Newton-Krylov Implementation of the Constrained Runs Scheme for Initializing on a Slow Manifold

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Abstract

The long-term dynamic behavior of many dynamical systems evolves on a low-dimensional, attracting, invariant slow manifold, which can be parameterized by only a few variables (“observables”). The explicit derivation of such a slow manifold (and thus, the reduction of the long-term system dynamics) is often extremely difficult or practically impossible. For this class of problems, the equation-free framework has been developed to enable performing coarse-grained computations, based on short full model simulations. Each full model simulation should be initialized so that the full model state is consistent with the values of the observables and close to the slow manifold. To compute such an initial full model state, a class of constrained runs functional iterations was proposed (Gear and Kevrekidis, J. Sci. Comput. 25(1), 17–28, 2005; Gear et al., SIAM J. Appl. Dyn. Syst. 4(3), 711–732, 2005). The schemes in this class only use the full model simulator and converge, under certain conditions, to an approximation of the desired state on the slow manifold. In this article, we develop an implementation of the constrained runs scheme that is based on a (preconditioned) Newton-Krylov method rather than on a simple functional iteration. The functional iteration and the Newton-Krylov method are compared in detail using a lattice Boltzmann model for one-dimensional reaction-diffusion as the full model simulator. Depending on the parameters of the lattice Boltzmann model, the functional iteration may converge slowly or even diverge. We show that both issues are largely resolved by using the Newton-Krylov method, especially when a coarse grid correction preconditioner is incorporated.

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References

  1. Bouras, A., Frayssé, V.: Inexact matrix-vector products in Krylov methods for solving linear systems: A relaxation strategy. SIAM J. Matrix Anal. Appl. 26(3), 660–678 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  2. Browning, G., Kreiss, H.-O.: Problems with different time scales for nonlinear partial differential equations. SIAM J. Appl. Math. 42(4), 704–718 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  3. Caiazzo, A.: Analysis of lattice Boltzmann initialization routines. J. Stat. Phys. 121(1–2), 37–48 (2005). Special Issue on Mesoscopic Methods in Engineering and Science. Guest Editors: Manfred Krafczyk, Anthony J.C. Ladd, and Li-Shi Luo

    Article  MATH  MathSciNet  Google Scholar 

  4. Chopard, B., Dupuis, A., Masselot, A., Luthi, P.: Cellular automata and lattice Boltzmann techniques: An approach to model and simulate complex systems. Adv. Complex Syst. 5(2–3), 103–246 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  5. Curry, J., Haupt, S.E., Limber, M.E.: Low-order models, initializations, and the slow manifold. Tellus A 47, 145–161 (1995)

    Article  Google Scholar 

  6. Danilov, V.G., Maslov, V.P., Volosov, K.A.: Mathematical Modelling of Heat and Mass Transfer Processes. Kluwer Academic, Dordrecht (1995)

    MATH  Google Scholar 

  7. Dawson, S.P., Chen, S., Doolen, G.D.: Lattice Boltzmann computations for reaction-diffusion equations. J. Chem. Phys. 98(2), 1514–1523 (1993)

    Article  Google Scholar 

  8. E, W.: Analysis of the heterogeneous multiscale method for ordinary differential equations. Commun. Math. Sci. 1(3), 423–436 (2003)

    MATH  MathSciNet  Google Scholar 

  9. Gear, C.W., Kevrekidis, I.G.: Projective methods for stiff differential equations: Problems with gaps in their eigenvalue spectrum. SIAM J. Sci. Comput. 24(4), 1091–1106 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  10. Gear, C.W., Kevrekidis, I.G.: Telescopic projective methods for parabolic differential equations. J. Comput. Phys. 187(1), 95–109 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  11. Gear, C.W., Kevrekidis, I.G.: Computing in the past with forward integration. Phys. Lett. A 321(5–6), 335–343 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  12. Gear, C.W., Kevrekidis, I.G.: Constraint-defined manifolds: A legacy code approach to low-dimensional computation. J. Sci. Comput. 25(1), 17–28 (2005)

    Article  MathSciNet  Google Scholar 

  13. Gear, C.W., Kaper, T.J., Kevrekidis, I.G., Zagaris, A.: Projecting to a slow manifold: Singularly perturbed systems and legacy codes. SIAM J. Appl. Dyn. Syst. 4(3), 711–732 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  14. Girimaji, S.S.: Reduction of large dynamical systems by minimization of evolution rate. Phys. Rev. Lett. 82, 2282–2285 (1999)

    Article  Google Scholar 

  15. Holmes, E.E., Lewis, M.A., Banks, J.E., Veit, R.R.: Partial differential equations in ecology: Spatial interactions and population dynamics. Ecology 75(1), 17–29 (1994)

    Article  Google Scholar 

  16. Kelley, C.T.: Iterative Methods for Linear and Nonlinear Equations. Frontiers in Applied Mathematics, vol.  16. Society for Industrial and Applied Mathematics, Philadelphia (1995)

    MATH  Google Scholar 

  17. Kevrekidis, I.G., Gear, C.W., Hyman, J.M., Kevrekidis, P.G., Runborg, O., Theodoropoulos, C.: Equation-free, coarse-grained multiscale computation: Enabling microscopic simulators to perform system-level analysis. Commun. Math. Sci. 1(4), 715–762 (2003)

    MATH  MathSciNet  Google Scholar 

  18. Kreiss, H.-O.: Problems with different time scales for ordinary differential equations. SIAM J. Numer. Anal. 16(6), 980–998 (1979)

    Article  MATH  MathSciNet  Google Scholar 

  19. Kreiss, H.-O.: Problems with different time scales. In: Brackbill, J.H., Cohen, B.I. (eds.) Multiple Time Scales, pp. 29–57. Academic, New York (1985)

    Google Scholar 

  20. Lee, S.L., Gear, C.W.: Second-order accurate projective integrators for multiscale problems. J. Comput. Appl. Math. 201(1), 258–274 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  21. Lorenz, E.N.: Attractor sets and quasi-geostrophic equilibrium. J. Atmos. Sci. 37, 1685–1699 (1980)

    Article  MathSciNet  Google Scholar 

  22. Mei, R., Luo, L.-S., Lallemand, P., d’Humières, D.: Consistent initial conditions for lattice Boltzmann simulations. Comput. Fluids 35(8–9), 855–862 (2006)

    Article  MathSciNet  Google Scholar 

  23. Nicolaides, R.A.: Deflation of conjugate gradients with applications to boundary value problems. SIAM J. Numer. Anal. 24(2), 355–365 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  24. Padiy, A., Axelsson, O., Polman, B.: Generalized augmented matrix preconditioning approach and its application to iterative solution of ill-conditioned algebraic systems. SIAM J. Matrix Anal. Appl. 22(3), 793–818 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  25. Parks, M.L., de Sturler, E., Mackey, G., Johnson, D., Maiti, S.: Recycling Krylov subspaces for sequences of linear systems. SIAM J. Sci. Comput. 28(5), 1651–1674 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  26. Patil, D.J., Hunt, B.R., Kalnay, E., Yorke, J.A., Ott, E.: Local low dimensionality of atmospheric dynamics. Phys. Rev. Lett. 86(26), 5878–5881 (2001)

    Article  Google Scholar 

  27. Qian, Y.H., Orszag, S.A.: Scalings in diffusion-driven reaction A+BC: Numerical simulations by lattice BGK models. J. Stat. Phys. 81(1–2), 237–253 (1995)

    Article  MATH  Google Scholar 

  28. Saad, Y.: A flexible inner-outer preconditioned GMRES algorithm. SIAM J. Sci. Stat. Comput. 14, 461–469 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  29. Saad, Y.: Iterative Methods for Sparse Linear Systems, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2003)

    MATH  Google Scholar 

  30. Saad, Y., Schultz, M.H.: GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM J. Sci. Stat. Comput. 7(3), 856–869 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  31. Shroff, G.M., Keller, H.B.: Stabilization of unstable procedures: The recursive projection method. SIAM J. Numer. Anal. 30(4), 1099–1120 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  32. Simoncini, V., Szyld, D.B.: Theory of inexact Krylov subspace methods and applications to scientific computing. SIAM J. Sci. Comput. 25(2), 454–477 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  33. Simoncini, V., Szyld, D.B.: On the occurrence of superlinear convergence of exact and inexact Krylov subspace methods. SIAM Rev. 47, 247–272 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  34. Succi, S.: The Lattice Boltzmann Equation for Fluid Dynamics and Beyond, 1st edn. Numerical Mathematics and Scientific Computation. Springer Series in Computational Mathematics, vol. 252. Oxford University Press, Oxford (2001)

    MATH  Google Scholar 

  35. Trefethen, L.N., Bau, D.: Numerical Linear Algebra. Society for Industrial and Applied Mathematics, Philadelphia (1997)

    MATH  Google Scholar 

  36. Trottenberg, U., Oosterlee, C., Schüller, A.: Multigrid. Academic, London (2001)

    MATH  Google Scholar 

  37. Van Leemput, P., Lust, K., Kevrekidis, I.G.: Coarse-grained numerical bifurcation analysis of lattice Boltzmann models. Phys. D: Nonlinear Phenom. 210(1–2), 58–76 (2005)

    MATH  Google Scholar 

  38. Van Leemput, P., Rheinländer, M., Junk, M.: Smooth initialization of lattice Boltzmann schemes. Comput. Math. Appl. (2007, accepted)

  39. Van Leemput, P., Vanroose, W., Roose, D.: Mesoscale analysis of the equation-free constrained runs initialization scheme. SIAM Multiscale Model. Simul. 6(4), 1234–1255 (2007)

    Article  Google Scholar 

  40. Vandekerckhove, C., Roose, D.: Accuracy analysis of acceleration schemes for stiff multiscale problems. J. Comput. Appl. Math. 211(2), 181–200 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  41. Vandekerckhove, C., Roose, D., Lust, K.: Numerical stability analysis of an acceleration scheme for step size constrained time integrators. J. Comput. Appl. Math. 200(2), 761–777 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  42. Vandekerckhove, C., Van Leemput, P., Roose, D.: Accuracy and stability of the coarse time-stepper for a lattice Boltzmann model. J. Algorithms Comput. Technol. 2(2), 249–273 (2008)

    Article  MathSciNet  Google Scholar 

  43. vanden Eshof, J., Sleijpen, G.L.G.: Inexact Krylov subspace methods for linear systems. SIAM J. Matrix Anal. Appl. 26(1), 125–153 (2005)

    Article  Google Scholar 

  44. Zagaris, A., Gear, C.W., Kaper, T.J., Kevrekidis, I.G.: Analysis of the accuracy and convergence of equation-free projection to a slow manifold (2007, submitted)

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Vandekerckhove, C., Kevrekidis, I. & Roose, D. An Efficient Newton-Krylov Implementation of the Constrained Runs Scheme for Initializing on a Slow Manifold. J Sci Comput 39, 167–188 (2009). https://doi.org/10.1007/s10915-008-9256-y

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  • DOI: https://doi.org/10.1007/s10915-008-9256-y

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