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FokkerPlanck

The Fokker-Planck equation is a partial differential equation that describes the time evolution of the probability density function of a continuous stochastic process. It is closely associated with diffusion and drift phenomena and is named after Adriaan Fokker and Max Planck, who developed and analyzed diffusion-type descriptions in the early 20th century. The equation can be derived from an underlying stochastic differential equation, such as a Langevin equation, and it provides a deterministic description of how probabilities spread over time.

In one spatial dimension, if a stochastic process Xt satisfies dXt = a(Xt,t) dt + b(Xt,t) dWt, where

∂t p(x,t) = - ∂x [ a(x,t) p(x,t) ] + (1/2) ∂x^2 [ b(x,t)^2 p(x,t) ].

Equivalently, with A(x,t) as drift and D(x,t) as diffusion,

∂t p = - ∂x [ A p ] + ∂x^2 [ D p ].

The (1/2) factor reflects Ito calculus conventions.

In multiple dimensions, with drift vector A(x,t) and diffusion matrix D(x,t), the equation becomes

∂t p = - ∑i ∂i [ Ai p ] + ∑i ∑j ∂i ∂j [ Dij p ].

The Fokker-Planck equation governs many physical, chemical, biological, and financial systems where stochastic dynamics lead to

Typical uses include finding stationary distributions, describing relaxation to equilibrium, and analyzing time-dependent probabilities in diffusion-reaction

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a
is
the
drift
and
b
is
the
diffusion
coefficient,
the
associated
probability
density
p(x,t)
evolves
according
to
evolving
probability
distributions.
It
is
related
to
the
Kolmogorov
forward
equation
and
to
the
master
equation
in
appropriate
limits.
systems.
Numerical
methods,
such
as
finite-difference
or
spectral
approaches,
are
commonly
employed
to
solve
the
equation
in
complex
settings.