Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T16:18:06.201Z Has data issue: false hasContentIssue false

An analysis of noise propagation in the multiscale simulation of coarse Fokker-Planck equations

Published online by Cambridge University Press:  30 November 2010

Yves Frederix
Affiliation:
Department of Computer Science, K.U. Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. Yves.Frederix@cs.kuleuven.be; Giovanni.Samaey@cs.kuleuven.be; Dirk.Roose@cs.kuleuven.be
Giovanni Samaey
Affiliation:
Department of Computer Science, K.U. Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. Yves.Frederix@cs.kuleuven.be; Giovanni.Samaey@cs.kuleuven.be; Dirk.Roose@cs.kuleuven.be
Dirk Roose
Affiliation:
Department of Computer Science, K.U. Leuven, Celestijnenlaan 200A, 3001 Leuven, Belgium. Yves.Frederix@cs.kuleuven.be; Giovanni.Samaey@cs.kuleuven.be; Dirk.Roose@cs.kuleuven.be
Get access

Abstract

We consider multiscale systems for which only a fine-scalemodel describing the evolution of individuals (atoms,molecules, bacteria, agents) is given, while we are interested in theevolution of the population density on coarse space and timescales. Typically, this evolution is described by a coarseFokker-Planck equation. In this paper, we consider a numerical procedure to compute the solution ofthis Fokker-Planck equation directly on the coarse level, based on the estimation of the unknown parameters (drift and diffusion) using only appropriately chosen realizations of the fine-scale,individual-basedsystem. As these parameters might be space- and time-dependent, theestimation is performed in every spatial discretization point and atevery time step. If the fine-scale model is stochastic, the estimationprocedure introduces noise on the coarse level.We investigate stability conditions for this procedure in thepresence of this noise and present ananalysis of the propagation of the estimation error in the numericalsolution of the coarse Fokker-Planck equation.

Type
Research Article
Copyright
© EDP Sciences, SMAI, 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ait-Sahalia, Y., Maximum likelihood estimation of discretely sampled diffusions: A closed-form approximation approach. Econometrica 70 (2002) 223262. CrossRef
Alber, M., Chen, N., Glimm, T. and Lushnikov, P.M., Multiscale dynamics of biological cells with chemotactic interactions: From a discrete stochastic model to a continuous description. Phys. Rev. E 73 (2006) 051901. CrossRef
E, W. and Engquist, B., The heterogeneous multi-scale methods. Commun. Math. Sci. 1 (2003) 87132. CrossRef
E, W., Liu, D. and Vanden-Eijnden, E., Analysis of multiscale methods for stochastic differential equations. Commun. Pure Appl. Math. 58 (2005) 15441585.
E, W., Engquist, B., Li, X., Ren, W. and Vanden-Eijnden, E., Heterogeneous multiscale methods: A review. Commun. Comput. Phys. 2 (2007) 367450.
Erban, R. and Othmer, H.G., From signal transduction to spatial pattern formation in E. coli: A paradigm for multiscale modeling in biology. SIAM Multiscale Model. Simul. 3 (2005) 362394. CrossRef
Fatkullin, I. and Vanden-Eijnden, E., A computational strategy for multiscale systems with applications to Lorenz 96 model. J. Comput. Phys. 200 (2004) 605638. CrossRef
Y. Frederix and D. Roose, A drift-filtered approach to diffusion estimation for multiscale processes, in Coping with complexity: model reduction and data analysis, Lecture Notes in Computational Science and Engineering 75, Springer-Verlag (2010).
Frederix, Y., Samaey, G., Vandekerckhove, C., Li, T., Nies, E. and Roose, D., Lifting in equation-free methods for molecular dynamics simulations of dense fluids. Discrete Continuous Dyn. Syst. Ser. B 11 (2009) 855874.
C. Gear, Projective integration methods for distributions. Technical report, NEC Research Institute (2001).
Gear, C.W., Kaper, T.J., Kevrekidis, I.G. and Zagaris, A., Projecting to a slow manifold: Singularly perturbed systems and legacy codes. SIAM J. Appl. Dyn. Syst. 4 (2005) 711732. CrossRef
Givon, D., Kupferman, R. and Stuart, A., Extracting macroscopic dynamics: model problems and algorithms. Nonlinearity 17 (2004) R55R127. CrossRef
Gray, R.M., Toeplitz and circulant matrices: A review. Found. Trends Commun. Inf. Theory 2 (2005) 155239. CrossRef
Jourdain, B., Bris, C.L. and Lelièvre, T., On a variance reduction technique for micro-macro simulations of polymeric fluids. J. Non-Newton. Fluid Mech. 122 (2004) 91106. CrossRef
Kevrekidis, I.G. and Samaey, G., Equation-free multiscale computation: Algorithms and applications. Ann. Rev. Phys. Chem. 60 (2009) 321344. CrossRef
Kevrekidis, I.G., Gear, C.W., Hyman, J.M., Kevrekidis, P.G., Runborg, O. and Theodoropoulos, C., Equation-free, coarse-grained multiscale computation: Enabling microscopic simulators to perform system-level analysis. Commun. Math. Sci. 1 (2003) 715762.
Öttinger, H.C., van den Brule, B.H.A.A. and Hulsen, M.A., Brownian configuration fields and variance reduced CONNFFESSIT. J. Non-Newton. Fluid Mech. 70 (1997) 255261. CrossRef
G. Pavliotis and A. Stuart, Multiscale Methods: Averaging and Homogenization, Texts in Applied Mathematics 53. Springer, New York (2007).
Pavliotis, G.A. and Stuart, A.M., Parameter estimation for multiscale diffusions. J. Stat. Phys. 127 (2007) 741781. CrossRef
Pokern, Y., Stuart, A.M. and Vanden-Eijnden, E., Remarks on drift estimation for diffusion processes. SIAM Multiscale Model. Simul. 8 (2009) 6995. CrossRef
H. Risken, The Fokker-Planck Equation: Methods of Solutions and Applications. Springer Series in Synergetics, Second Edition, Springer (1989).
M. Rousset and G. Samaey, Simulating individual-based models of bacterial chemotaxis with asymptotic variance reduction. INRIA, inria-00425065, available at http://hal.inria.fr/inria-00425065/fr/ (2009).
A. Skorokhod, Asymptotic methods in the theory of stochastic differential equations, Translations of mathematical monographs 78. AMS, Providence (1999).
Van Kampen, N., Elimination of fast variables. Phys. Rep. 124 (1985) 69160. CrossRef
Van Leemput, P., Vanroose, W. and Roose, D., Mesoscale analysis of the equation-free constrained runs initialization scheme. SIAM Multiscale Model. Simul. 6 (2007) 12341255. CrossRef
Vanden-Eijnden, E., Numerical techniques for multi-scale dynamical systems with stochastic effects. Commun. Math. Sci. 1 (2003) 385391. CrossRef