Nonlinear Fokker-Planck Equations: Fundamentals and Applications (Springer Series in Synergetics) 2005th Edition by T.D. Frank (PDF)

Description

Centered around the natural phenomena of relaxations and fluctuations, this monograph provides readers with a solid foundation in the linear and nonlinear Fokker-Planck equations that describe the evolution of distribution functions. It emphasizes principles and notions of the theory (e.g. self-organization, stochastic feedback, free energy, and Markov processes), while also illustrating the wide applicability (e.g. collective behavior, multistability, front dynamics, and quantum particle distribution). The focus is on relaxation processes in homogeneous many-body systems describable by nonlinear Fokker-Planck equations. Also treated are Langevin equations and correlation functions. Since these phenomena are exhibited by a diverse spectrum of systems, examples and applications span the fields of physics, biology and neurophysics, mathematics, psychology, and biomechanics.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐This book is riddled with very serious mistakes, as are many of the papers it refers to. There is no such thing as a ‘nonlinear Markov process’. There is no such thing as a ‘nonlinear Fokker-Planck equation’ for a conditional probability. A conditional probability with initial state memory is nonMarkovian. A conditional probability with initial state memory is not guaranteed to obey a Chapman-Kolmogorov equation and usually doesn’t. A Chapman-Kolmogorov equation is a necessary but not sufficient condition for a Markov process. A Fokker-Planck equation with memory of an initial state in its drift and/or diffusion coefficients does not generate a Markov process. A nonlinear diffusion equation does not define any stochastic process at all, in fact a diffusion equation for a 1-point density defines no stochastic process at all. A 1-point density cannot be used to identify/define a stochastic process, both scaling Markov processes and strongly nonMarkov processes like fractional Brownian motion have exactly the same 1-point density, with widely differing conditional densities. For detailed explanations see cond-mat/0701589.

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