Langevin to Fokker-planck? Uh oh

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In summary, the conversation discusses the Langevin equation, which describes the motion of a particle in a fluctuating environment, and the Fokker-Planck equation, which is a time evolution of a probability density. The two equations have some differences in their approach, but ultimately yield identical expressions for diffusivity. The conversation also references other sources for further information on this topic.
  • #1
JVanUW
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This isn't homework but I'm interested.
So I have the langevin equation dy/dt = -dV/dy +η(t)

where V(y) = -by^3/3 + ζy

how can I turn this into a fokker-planck equation?

What I'm getting is

x' = -u(bx^2+ζ) + η(t)

Which I don't know how to solve in closed form.

Any ideas/suggestions?

Thanks!
 
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  • #2
Yikes... I have some information, but it's not clear how to relate the specific to your question. I have the Langevin equation as m dv/dt = F + f, where f is the randomly fluctuating force. The Fokker-Planck equation is based on the continuity equation and is too messy to write here, but is a time evolution of a probability density. The long-time limit is known as the Smoluchowski equation.

The two equations (Langevin vs. Fokker-Planck/Smoluchowski) differ in a few respects- primarily the relevant timescale, but also the difference in viewing a process as 'diffusive' or as direct modeling of fluctuations in particle velocity.

My reference for this is Brenner and Edwards, "Macrotransport Processes". They begin by defining a timescale that allows for an average velocity, but short enough to allow for fluctuations. This leads to an integral expression for the probability density, and after a few short (but very dense) pages, they show the two approaches yield identical expressions for the diffusivity.

They reference Masters, "Time-scale separations and the validity of the Smoluchowski, Fokker-Planck and Langevin equations as applied to concentrated particle suspensions" Mol. Phys. 57, 303-317 (1986). There may also be some useful material in Balescu's "Statistical Dynamics: Matter out of Equilibrium"- it seems to be an open area of research (by considering Levy distributions instead of Gaussian distributions, for example)
 
  • #3
I'll check it out thanks!
 

1. What is the Langevin to Fokker-Planck equation and what does it represent?

The Langevin to Fokker-Planck equation is a mathematical model that describes the behavior of a particle in a fluid environment. It takes into account both deterministic forces, such as friction and external forces, and random forces, such as thermal fluctuations. Essentially, it represents the motion of a particle under the influence of both deterministic and random forces.

2. How is the Langevin to Fokker-Planck equation derived?

The Langevin to Fokker-Planck equation is derived from the Langevin equation, which describes the motion of a particle in terms of its position, velocity, and acceleration. By applying statistical mechanics principles, the Langevin equation can be transformed into the Fokker-Planck equation, which describes the probability density function of the particle's position and velocity over time.

3. What is the significance of the Langevin to Fokker-Planck equation in physics?

The Langevin to Fokker-Planck equation is important in many areas of physics, including statistical mechanics, fluid dynamics, and quantum mechanics. It allows scientists to study the behavior of particles in complex systems and understand the effects of both deterministic and random forces on their motion.

4. How is the Langevin to Fokker-Planck equation used in practical applications?

The Langevin to Fokker-Planck equation has many practical applications, such as in modeling diffusion processes, understanding the behavior of Brownian motion, and studying the dynamics of chemical reactions. It is also used in fields such as finance and economics to model the behavior of stock prices and other financial variables.

5. What are the limitations of the Langevin to Fokker-Planck equation?

While the Langevin to Fokker-Planck equation is a powerful tool for understanding the behavior of particles in complex systems, it does have some limitations. It assumes that the particle is in thermal equilibrium with its environment and that the forces acting on the particle are time-independent. It also does not take into account quantum effects, so it is not applicable in situations where these effects are significant.

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