Structural modeling of diffusion processes
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Abstract
We try to give a stochastic description for a system which is in contact with a reservoir if the deterministic phenomenological description is given. We compare the phenomenological method, i.e. the enlargement of the phenomenological equation by stochastic parts leading to a stochastic differential equation and the reduction of the microscopic description in the scope of classical physics. The projector method gives a description of the system by a Fokker-Planck equation. The essential facts characterizing a diffusion process are extracted from the structure of these equations. If the systematic part of the phenomenological equation can be derived from generalized Poisson brackets it is possible to give criteria for an ansatz of a stochastic description which is self consistent and in accordance with the results of information theory.
Keywords
Differential Equation Neural Network Complex System Information Theory Nonlinear DynamicsPreview
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References
- 1.Graham, R.: Springer Tracts in Modern Physics, Vol. 66, 1. Berlin-Heidelberg-New York: Springer 1973Google Scholar
- 2.Haken, H.: Synergetics. Berlin-Heidelberg-New York: Springer 1977Google Scholar
- 3.Zwanzig, R.: Phys. Rev.124, 983 (1961)Google Scholar
- 4.Mori, H.: Prog. Theor. Phys.33, 423 (1965)Google Scholar
- 5.Nicolis, G., Prigogine, I.: Proc. Nat. Acad. Sci. USA66, 2102 (1971)Google Scholar
- 6.Nicolis, G.: J. Stat. Phys.6, 195 (1972)Google Scholar
- 7.Kerner, E.H.: Adv. Chem. Phys.19, 325 (1971)Google Scholar
- 8.Goel, N.S., Maitra, S.C., Montroll, E.W.: Rev. Mod. Phys.43, 231 (1971)Google Scholar
- 9.May, R.M.: Stability and Complexity in Model Ecosystems. Princeton: Princeton University Press 1974Google Scholar
- 10.Reddy, V.T.N.: J. Stat. Phys.13, 61 (1975)Google Scholar
- 11.Arnold, L.: Stochastische Differentialgleichungen. München: Oldenbourg 1973Google Scholar
- 12.McGarty, T.P.: Stochastic Systems and State Estimation. New York: Wiley 1974Google Scholar
- 13.Wong, E., Zakai, M.: Ann. Math. Statist.36, 1560 (1965)Google Scholar
- 14.Stratonovich, R.L.: Conditional Marcov Processes and their Application to the Theory of Optimal Control. New York: Elsevier 1968Google Scholar
- 15.Nordholm, J.S., Zwanzig, R.: J. Stat. Phys.13, 347 (1975)Google Scholar
- 16.Onsager, L.: Phys. Rev.37, 405 (1931)Google Scholar
- 17.Zwanzig, R.: J. Stat. Phys.9, 215 (1973)Google Scholar
- 18.Sudarshan, E.C.G., Munkunda, N.: Classical Dynamics: A Modern Perspective. New York: Wiley 1974Google Scholar
- 19.Jaynes, E.T.: Information Theory and Statistical Mecanics. In: ed. K.W. Ford: Brandeis University Summer Institute Lectures in Theoretical Physics, 1962, Vol. 3, 181. New York: Benjamin 1963Google Scholar
- 20.Hirsch, M.W., Smale, S.: Differential Equations, Dynamical Systems and Linear Algebra. New York: Academic Press 1974Google Scholar
- 21.Smith, E., Tuckwell, H.C.: Lecture Notes in Boimathematics, Vol. 2, 211. Berlin-Heidelberg-New York: Springer 1974Google Scholar
- 22.Wöhrstein, H.G.: Diss. Stuttgart 1974Google Scholar
- 23.Nakamura, K.: J. Phy. Soc. Jap.38, 46 (1975)Google Scholar
- 24.Enz, C.P.: Lecture Notes in Physics, Vol. 54, 79. Berlin-Heidelberg-New York: Springer 1976Google Scholar