Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-27T21:35:38.771Z Has data issue: false hasContentIssue false

Resolving the Multitude of Microscale Interactions Accurately Models Stochastic Partial Differential Equations

Published online by Cambridge University Press:  01 February 2010

A. J. Roberts
Affiliation:
Research Centre, Department of Mathematics & Computing, University of Southern Queensland, Toowoomba, Queensland 4352, Australia, aroberts@usq.edu.au, http://www.sci.usq.edu.au/staff/aroberts

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Constructing numerical models of noisy partial differential equations is a very delicate task. Our long-term aim is to use modern dynamical systems theory to derive discretisations of dissipative stochastic partial differential equations. As a second step, we consider here a small domain, representing a finite element, and derive a one-degree-of-freedom model for the dynamics in the element; stochastic centre manifold theory supports the model. The approach automatically parametrises the microscale structures induced by spatially varying stochastic noise within the element. The crucial aspect of this work is that we explore how a multitude of microscale noise processes may interact in nonlinear dynamical systems. The analysis finds that noise processes with coarse structure across a finite element are the significant noises for the modelling. Further, the nonlinear dynamics abstracts effectively new noise sources over the macroscale time-scales resolved by the model.

Type
Research Article
Copyright
Copyright © London Mathematical Society 2006

References

1. Arnold, Ludwig, Random dynamical systems, Springer Monogr. Malh. (Springer, 2003) ISBN 3–540–63758–3.Google Scholar
2. Arnold, L., Sri Namachchivaya, N. and Schenk-Hoppé, K. R., ‘Toward an understanding of stochastic Hopf bifurcation: a case study’, Internal. J. Bifur. Chaos Appl. Sci. Engrg. 6 (1996) 19471975.CrossRefGoogle Scholar
3. Baxter, Martin and Rennie, Andrew, Financial calculus: An introduction to derivative pricing (Cambridge University Press, 1996).CrossRefGoogle Scholar
4. Bensoussan, A. and Flandoli, F., ‘Stochastic inertial manifold’. Stochastics Stochastics Rep. 53 (1995) 1339.CrossRefGoogle Scholar
5. Berglund, Nils and Gentz, Barbara, ‘Geometric singular perturbation theory for stochastic differential equations’, J. Differential Equations 191 (2003) 154, http://dx.doi.org/10.1016/S0022-0396(03)0002 0-2.CrossRefGoogle Scholar
6. Blomker, D., Hairer, M. and Pavliotis, G. A., ‘Modulation equations: stochastic bifurcation in large domains’, Comm. Math. Phys. 258 (2005) 479512, http://dx.doi.org/10.1007/s00220-005-1368-8.CrossRefGoogle Scholar
7. Boxler, P., ‘A stochastic version of the centre manifold theorem’. Probab. Theory Related Fields 83 (1989) 509545.Google Scholar
8. Boxler, P., How to construct stochastic center manifolds on the level of vector fields, Lecture Notes in Math. 1486 (Springer, 1991) 141158.Google Scholar
9. Caraballo, Tomas, Langa, Jose A. and Robinson, James C., ‘A stochastic pitchfork bifurcation in a reaction-diffusion equation’, Proc. Roy. Soc. Lond. A 457 (2001) 20412061. http://dx.doi.org/10.1098/rspa.2001.0819.CrossRefGoogle Scholar
10. Chao, Xu and Roberts, A. J., ‘On the low-dimensional modelling of Stratonovich stochastic differential equations’, Physica A 225 (1996) 6280, http://dx.doi.org/10.1016/0378-4371(95)00387-8.Google Scholar
11. Chatwin, P. C., ‘The approach to normality of the concentration distribution of a solute in a solvent flowing along a straight pipe’. J. Fluid Mech. 43 1970) 321’352.Google Scholar
12. Chicone, C. and Latushkin, Y., ‘Center manifolds for infinite dimensional non-autonomous differential equations’, J. Differential Equations 141 (1997) 356399. http://www.ingentaconnect.com/content/ap/de/1997/00000141/00000002/art03343.CrossRefGoogle Scholar
13. Coullet, P. H., Elphick, C. and Tirapegui, E., ‘Normal form of a Hopf bifurcation with noise’, Phys. Lett. 111 A (1985) 277282.CrossRefGoogle Scholar
14. Drolet, Francois and Vinals, Jorge, ‘Adiabatic reduction near a bifurcation in stochastically modulated systems’, Phys. Rev. E 57 (1998) 50365043. http://link.aps.org/abstract/PRE/v57/p5036.Google Scholar
15. Drolet, Francois and Vinals, Jorge, ‘Adiabatic elimination and reduced probability distribution functions in spatially extended systems with a fluctuating control parameter’, Phys. Rev. E 64 (2001) 026120, http://link.aps.org/abstract/PRE/v64/e026120.Google Scholar
16. Duan, Jinqiao, Lu, Kening and Schmalfuss, Bjorn, Invariant manifolds for stochastic partial differential equations’, Ann. Probab. 31 (2003) 21092135.Google Scholar
17. Gallay, Th., ‘A center-stable manifold theorem for differential equations in Banach spaces’. Comm. Math. Phys 152 (1993) 249268.CrossRefGoogle Scholar
18. Grecksch, W. and Kloeden, P. E., ‘Time-discretised Galerkin approximations of parabolic stochastics PDEs’, Bull. Austral Math. Soc. 54 (1996) 7985.CrossRefGoogle Scholar
19. Just, Wolfram, Kantz, Holger, Rodenbeck, Christian and Helm, Mario, ‘Stochastic modelling: replacing fast degrees of freedom by noise’. J. Phys. A: Math. Gen. 34 (2001) 31993213.CrossRefGoogle Scholar
20. Kabanov, Yuri and Pergamenshchikov, Sergei, Two-scale stochastic systems, Applications of Mathematics: Stochastic Modelling and Applied Probability 49 (Springer. 2003).CrossRefGoogle Scholar
21. Kloeden, P. E. and Platen, E., Numerical solution of stochastic differential equations, Appl. Math. 23 (Springer. 1992).CrossRefGoogle Scholar
22. Knobloch, E. and Wiesenfeld, K. A., ‘Bifurcations in fluctuating systems: The centre manifold approach’. J. Statist. Phys 33 (1983) 611637.Google Scholar
23. MacKenzie, T. and Roberts, A. J., ‘Holistic finite differences accurately model the dynamics of the Kuramoto-Sivashinsky equation’, ANZ1AM J. 42 (2000) C918–C935. http://anziamj.austms.org.au/V42/CTAC99/Mack.Google Scholar
24. MacKenzie, T. and Roberts, A. J., ‘Holistic discretisation of shear dispersion in a two-dimensional channel’, Proc, 10th Computational Techniques and Applications Conference CTAC-2001. ed. Burrage, K. and Sidje, Roger B., ANZIAM J. 44C (2003) C512–C530, http://anziamj.austms.org.au/V44/CTAC2001/Mack.Google Scholar
25. Metzler, R., ‘Non-homogeneous random walks, generalised master equations, fractional Fokker-Planck equations, and the generalised Kramers-Moyal expansion’, Eur. Phys. J. B 19 (2001) 249258. http://www.edpsciences.org/articles/epjb/pdf/2001/02/b0331.pdf.CrossRefGoogle Scholar
26. Naert, A., Friedrich, R. and Peinke, J., ‘Fokker-Planck equation for the energy cascade in turbulence’, Phys. Rev. E 56 (1997) 67196722.CrossRefGoogle Scholar
27. Pollett, P. K. and Roberts, A.J., ‘A description of the long-term behaviour of absorbing continuous time Markov chains using a centre manifold’. Adv. in Appl. Probab. 22 (1990) 111‘128.Google Scholar
28. Roberts, A. J., ‘The application of centre manifold theory to the evolution of systems which vary slowly in space’. J. Austral. Math. Soc. B 29 (1988) 480500.Google Scholar
29. Roberts, A. J., ‘Low-dimensional modelling of dynamics via computer algebra’, Comput. Phys. Comm. 100 (1997) 215230.Google Scholar
30. Roberts, A. J., ‘Holistic discretisation ensures fidelity to Burgers' equation’. Appl. Numer. Modelling 37 (2001) 371396. http://arXiv.org/abs/chao-dyn/9901011.Google Scholar
31. Roberts, A. J., ‘Holistic projection of initial conditions onto a finite difference approximation’. Comput. Phys. Comm. 142 (2001) 316321, http://arXiv.org/abs/math.NA/0101205.Google Scholar
32. Roberts, A. J., ‘A holistic finite difference approach models linear dynamics consistently’, Math. Comp. 72 (2002) 247262. http://arXiv.org/abs/math.NA/0003135.CrossRefGoogle Scholar
33. Roberts, A. J., ‘Derive boundary conditions for holistic discretisations of Burgers' equation’, Proc, 10th Computational Techniques and Applications Conference CTAC-2001, ed. Burrage, K. and Sidje, Roger B., ANZIAM J. 44C (2003) C664C686. http://anziamj.austms.org.au/V44/CTAC2001/Robe.Google Scholar
34. Roberts, A. J., ‘Low-dimensional modelling of dynamical systems applied to some dissipative fluid mechanics’, Nonlinear dynamics from lasers to butterflies, ed.Ball, Rowena and Akhmediev, Nail, Lecture Notes in Complex Systems I (World Scientific, 2003) 257313.CrossRefGoogle Scholar
35. Roberts, A. J., ‘A step towards holistic discretisation of stochastic partial differential equations’. Proc, 11th Computational Techniques and Applications Conference CTAC-2003 (Dec. 2003). ed. Crawford, Jagoda and Roberts, A. J., ANZIAM J. 45 (2004) C1–C15, http://anziamj.austms.org.au/V45/CTAC2003/Robe.Google Scholar
36. Roberts, A. J., ‘Computer algebra resolves a multitude of microscale interactions to model stochastic partial differential equations’. Technical report. University of Southern Queensland. December 2005. http://www.sci.usq.edu.au/staff/robertsa/CA/multinoise.pdf.Google Scholar
37. Robinson, J. C., ‘The asymptotic completeness of inertial manifolds’, Nonlinearity 9 (1996) 13251340, http://www.iop.org/EJ/abstract/0951-7715/9/5/013.Google Scholar
38. Schöner, G. and Haken, H., ‘The slaving principle for Stratonovich stochastic differential equations’, Z Phys. B — Condensed matter 63 (1986) 493504.CrossRefGoogle Scholar
39. Sri Namachchivaya, N. and Lin, Y. K., ‘Method of stochastic normal forms’, Int. J. Nonlinear Mechanics 26 (1991) 931943.CrossRefGoogle Scholar
40. Tutkun, M. and Mydlarski, L., ‘Markovian properties of passive scalar increments in grid-generated turbulence’, New J. Phys. 6 (2004) article 49, 1–24. http://dx.doi.org/10.1088/1367-2360/6/1/049.Google Scholar
41. Vanden-Eijnden, Eric, ‘Asymptotic techniques for SDEs’. Fast times and fine scales: Proc, 2005 Program in Geophysical Fluid Dynamics (Woods Hole Oceanographic Institution, 2005), http://gfd.whoi-edu/proceedings/2005/PDFvol2005.html.Google Scholar
42. Werner, M. J. and Drummond, P. D., ‘Robust algorithms for solving stochastic partial differential equations’. J. Comput. Phys. 132 (1997) 312326.CrossRefGoogle Scholar