Fokker-Planck equation
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The Fokker-Planck equation (named after Adriaan Fokker and Max Planck; also known as the Kolmogorov Forward equation) describes the time evolution of the probability density function of position and velocity of a particle.
The first use of the Fokker-Planck equation was the statistical description of Brownian motion of a particle in a fluid.
Brownian motion follows the Langevin equation, which can be solved for many different stochastic forcings with results being averaged (the Monte Carlo Method).
However, instead of this computationally intensive approach, one can use the Fokker-Planck equation and consider [\mathbf(\mathbf, t)], that is, the probability density function of the particle having a velocity in the interval [(\mathbf, \mathbf + \mathbf)], when it starts its motion with [\mathbf] at time [t_0].
The general form of the Fokker-Planck equation for N variables is
- [\frac} = \left[-sum_^ frac D_i^1(x_1, ldots, x_N) + sum_^ sum_^ frac D_^2(x_1, ldots, x_N) right] \mathbf,]
Relationship with Stochastic Differential Equations
The Fokker-Planck equation can be used for computing the probability densities of stochastic differential equations. Consider the Itô stochastic differential equation
[dX_t = \mu(X_t,t) dt + \sigma(X_t,t) dB_t,]
where [X_t \in \mathbb^N] is the state and [B_t \in \mathbb^M] is a standard M-dimensional Brownian motion. If the initial distribution is [X_0 \sim \mathbf(X_0,0)], then the probability density of the state [X_t] of is given by the Fokker-Planck equation with the diffusion and drift terms
[D^1_i(X_t,t) = \mu_i(X_t,t)]
[D^2_(X_t,t) = \frac \sum_k \sigma_(X_t,t) \sigma_(X_t,t).]
Examples
A standard scalar Brownian motion is generated by the stochastic differential equation
[ dX_t = dB_t,]
Now the drift term is zero and diffusion coefficient is 1/2 and thus the corresponding Fokker-Planck equation is
[ \frac(X_t,t)} = \frac \frac(X_t,t)},]
which is the simplest form of diffusion equation.
External links
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