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# Fokker–Planck equation

In statistical mechanics, the Fokker–Planck equation is a partial differential equation that describes the time evolution of the probability density function of the velocity of a particle under the influence of drag forces and random forces, as in Brownian motion. The equation can be generalized to other observables as well.[1] It is named after Adriaan Fokker and Max Planck,[2][3] and is also known as the Kolmogorov forward equation, after Andrey Kolmogorov, who independently discovered the concept in 1931.[4] When applied to particle position distributions, it is better known as the Smoluchowski equation (after Marian Smoluchowski), and in this context it is equivalent to the convection–diffusion equation. The case with zero diffusion is known in statistical mechanics as the Liouville equation. The Fokker–Planck equation is obtained from the master equation through Kramers–Moyal expansion.

The first consistent microscopic derivation of the Fokker–Planck equation in the single scheme of classical and quantum mechanics was performed by Nikolay Bogoliubov and Nikolay Krylov.[5][6]

The Smoluchowski equation is the Fokker–Planck equation for the probability density function of the particle positions of Brownian particles.[7]

## One dimension

In one spatial dimension x, for anItō processdriven by the standardWiener processand described by thestochastic differential equation(SDE)
withdriftanddiffusioncoefficient, the Fokker–Planck equation for the probability densityof the random variableis
In the following, use.
Define theinfinitesimal generator(the following can be found in Ref.[8]):
The transition probability, the probability of going fromto, is introduced here; the expectation can be written as
Now we replace in the definition of, multiply byand integrate over. The limit is taken on

Note now that

which is the Chapman–Kolmogorov theorem. Changing the dummy variableto, one gets

which is a time derivative. Finally we arrive to

From here, the Kolmogorov backward equation can be deduced. If we instead use the adjoint operator of,, defined such that
then we arrive to the Kolmogorov forward equation, or Fokker–Planck equation, which, simplifying the notation, in its differential form reads
Remains the issue of defining explicitly. This can be done taking the expectation from the integral form of theItō's lemma:
The part that depends onvanished because of the martingale property.

Then, for a particle subject to an Itō equation, using

it can be easily calculated, using integration by parts, that

which bring us to the Fokker–Planck equation:

While the Fokker–Planck equation is used with problems where the initial distribution is known, if the problem is to know the distribution at previous times, the Feynman–Kac formula can be used, which is a consequence of the Kolmogorov backward equation.

The stochastic process defined above in the Itō sense can be rewritten within the Stratonovich convention as a Stratonovich SDE:

It includes an added noise-induced drift term due to diffusion gradient effects if the noise is state-dependent. This convention is more often used in physical applications. Indeed, it is well known that any solution to the Stratonovich SDE is a solution to the Itō SDE.

The zero-drift equation with constant diffusion can be considered as a model of classical Brownian motion:

This model has discrete spectrum of solutions if the condition of fixed boundaries is added for:

It has been shown[9] that in this case an analytical spectrum of solutions allows deriving a local uncertainty relation for the coordinate-velocity phase volume:

Hereis a minimal value of a corresponding diffusion spectrum, whileandrepresent the uncertainty of coordinate–velocity definition.

## Higher dimensions

More generally, if

whereandare N-dimensional randomvectors,is an NM matrix andis an M-dimensional standardWiener process, the probability densityforsatisfies the Fokker–Planck equation
with drift vectorand diffusiontensor, i.e.

If instead of an Itō SDE, a Stratonovich SDE is considered,

the Fokker–Planck equation will read ([8] pag. 129):

## Examples

### Wiener process

A standard scalar Wiener process is generated by the stochastic differential equation

Here the drift term is zero and the diffusion coefficient is 1/2. Thus the corresponding Fokker–Planck equation is

which is the simplest form of adiffusion equation. If the initial condition is, the solution is

### Ornstein–Uhlenbeck process

The Ornstein–Uhlenbeck process is a process defined as

.
with. The corresponding Fokker–Planck equation is
The stationary solution () is

### Plasma physics

In plasma physics, thedistribution functionfor a particle species,, takes the place of theprobability density function. The corresponding Boltzmann equation is given by
where the third term includes the particle acceleration due to theLorentz forceand the Fokker–Planck term at the right-hand side represents the effects of particle collisions. The quantitiesandare the average change in velocity a particle of typeexperiences due to collisions with all other particle species in unit time. Expressions for these quantities are given elsewhere.[10] If collisions are ignored, the Boltzmann equation reduces to theVlasov equation.

## Computational considerations

Brownian motion follows theLangevin equation, which can be solved for many different stochastic forcings with results being averaged (theMonte Carlo method, canonical ensemble inmolecular dynamics). However, instead of this computationally intensive approach, one can use the Fokker–Planck equation and consider the probabilityof the particle having a velocity in the intervalwhen it starts its motion withat time 0.

## Solution

Being apartial differential equation, the Fokker–Planck equation can be solved analytically only in special cases. A formal analogy of the Fokker–Planck equation with theSchrödinger equationallows the use of advanced operator techniques known from quantum mechanics for its solution in a number of cases. Furthermore, in the case of overdamped dynamics when the Fokker–Planck equation contains second partial derivatives with respect to all variables, the equation can be written in the form of amaster equationthat can easily be solved numerically [11]. In many applications, one is only interested in the steady-state probability distribution, which can be found from. The computation of meanfirst passage timesand splitting probabilities can be reduced to the solution of an ordinary differential equation which is intimately related to the Fokker–Planck equation.

## Particular cases with known solution and inversion

Inmathematical financeforvolatility smilemodeling of options vialocal volatility, one has the problem of deriving a diffusion coefficientconsistent with a probability density obtained from market option quotes. The problem is therefore an inversion of the Fokker–Planck equation: Given the density f(x,t) of the option underlying X deduced from the option market, one aims at finding the local volatilityconsistent with f. This is aninverse problemthat has been solved in general by Dupire (1994, 1997) with a non-parametric solution. Brigo and Mercurio (2002, 2003) propose a solution in parametric form via a particular local volatilityconsistent with a solution of the Fokker–Planck equation given by amixture model. More information is available also in Fengler (2008), Gatheral (2008) and Musiela and Rutkowski (2008).

## Fokker–Planck equation and path integral

Every Fokker–Planck equation is equivalent to a path integral. The path integral formulation is an excellent starting point for the application of field theory methods.[12] This is used, for instance, in critical dynamics.

A derivation of the path integral is possible in a similar way as in quantum mechanics. The derivation for a Fokker–Planck equation with one variable x is as follows. Start by inserting a delta function and then integrating by parts:

The x-derivatives here only act on the-function, not on. Integrate over a time interval,

Insert the Fourier integral

for the-function,
This equation expressesas functional of. Iteratingtimes and performing the limitgives a path integral withaction
The variablesconjugate toare called "response variables".[13]

Although formally equivalent, different problems may be solved more easily in the Fokker–Planck equation or the path integral formulation. The equilibrium distribution for instance may be obtained more directly from the Fokker–Planck equation.

• Kolmogorov backward equation

• Boltzmann equation

• Vlasov equation

• Master equation

• Mean field game theory

• Bogoliubov–Born–Green–Kirkwood–Yvon hierarchy of equations

• Ornstein–Uhlenbeck process

• Convection–diffusion equation

## References

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Citation Link//doi.org/10.1002%2Fandp.19143480507Fokker, A. D. (1914). "Die mittlere Energie rotierender elektrischer Dipole im Strahlungsfeld". Ann. Phys. 348 (4. Folge 43): 810–820. Bibcode:1914AnP...348..810F. doi:10.1002/andp.19143480507.
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Citation Linkbiodiversitylibrary.orgPlanck, M. (1917). "Über einen Satz der statistischen Dynamik und seine Erweiterung in der Quantentheorie". Sitzungsberichte der Preussischen Akademie der Wissenschaften zu Berlin. 24: 324–341.
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Citation Link//doi.org/10.1007%2FBF01457949Kolmogorov, Andrei (1931). "Über die analytischen Methoden in der Wahrscheinlichkeitstheorie" [On Analytical Methods in the Theory of Probability]. Mathematische Annalen (in German). 104 (1): 415–458 [pp. 448–451]. doi:10.1007/BF01457949.
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Citation Linkopenlibrary.orgN. N. Bogoliubov and N. M. Krylov (1939). Fokker–Planck equations generated in perturbation theory by a method based on the spectral properties of a perturbed Hamiltonian. Zapiski Kafedry Fiziki Akademii Nauk Ukrainian SSR 4: 81–157 (in Ukrainian).
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Citation Linkbooks.google.comDhont, J. K. G. (1996). An Introduction to Dynamics of Colloids. Elsevier. p. 183. ISBN 978-0-08-053507-4.
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Citation Link//doi.org/10.1155%2F2014%2F292096Kamenshchikov, S. (2014). "Clustering and Uncertainty in Perfect Chaos Systems". Journal of Chaos. 2014: 1–6. arXiv:1301.4481. doi:10.1155/2014/292096.
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Citation Link//doi.org/10.1103%2Fphysrev.107.1Rosenbluth, M. N. (1957). "Fokker–Planck Equation for an Inverse-Square Force". Physical Review. 107 (1): 1–6. Bibcode:1957PhRv..107....1R. doi:10.1103/physrev.107.1.
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Citation Link//www.ncbi.nlm.nih.gov/pubmed/30999402Holubec Viktor, Kroy Klaus, and Steffenoni Stefano (2019). "Physically consistent numerical solver for time-dependent Fokker-Planck equations". Phys. Rev. E. 99 (4): 032117. doi:10.1103/PhysRevE.99.032117. PMID 30999402.CS1 maint: multiple names: authors list (link)
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