Stochastic partial differential equation


Stochastic partial differential equations (SPDEs) generalize partial differential equations via random force terms and coefficients, in the same way ordinary stochastic differential equations generalize ordinary differential equations.

They have relevance to quantum field theory, statistical mechanics, and spatial modeling.[1][2]


One of the most studied SPDEs is the stochastic heat equation, which may formally be written as

where is the Laplacian and denotes space-time white noise. Other examples also include stochastic versions of famous linear equations, such as wave equation and Schrödinger equation.


One difficulty is their lack of regularity. In one dimensional space, solutions to the stochastic heat equation are only almost 1/2-Hölder continuous in space and 1/4-Hölder continuous in time. For dimensions two and higher, solutions are not even function-valued, but can be made sense of as random distributions.

For linear equations, one can usually find a mild solution via semigroup techniques.[3]

However, problems start to appear when considering non-linear equations. For example

where is a polynomial. In this case it is not even clear how one should make sense of the equation. Such an equation will also not have a function-valued solution, and hence no pointwise meaning. It is well known that the space of distributions has no product structure. This is the core problem of such a theory. This leads to the need of some form of renormalization.

An early attempt to circumvent such problems for some specific equations was the so called da Pratto-Debusche trick which involved studying such non-linear equations as perturbations of linear ones. However, this can only be used in very restrictive settings, as it depends on both the non-linear factor and on the regularity of the driving noise term. In recent years, the field has drastically expanded, and now there exists a large machinery to guarantee local existence for a variety of sub-critical SPDE's.

See also


  1. ^ Prévôt, Claudia; Röckner, Michael (2007). A Concise Course on Stochastic Partial Differential Equations. Lecture Notes in Mathematics. Berlin Heidelberg: Springer-Verlag. ISBN 978-3-540-70780-6.
  2. ^ Krainski, Elias T.; Gómez-Rubio, Virgilio; Bakka, Haakon; Lenzi, Amanda; Castro-Camilo, Daniela; Simpson, Daniel; Lindgren, Finn; Rue, Håvard (2018). Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA. Boca Raton, FL: Chapman and Hall/CRC Press. ISBN 978-1-138-36985-6.
  3. ^ Walsh, John B. (1986). Carmona, René; Kesten, Harry; Walsh, John B.; Hennequin, P. L. (eds.). "An introduction to stochastic partial differential equations". École d'Été de Probabilités de Saint Flour XIV - 1984. Lecture Notes in Mathematics. Springer Berlin Heidelberg. 1180: 265–439. doi:10.1007/bfb0074920. hdl:10338.dmlcz/126035. ISBN 978-3-540-39781-6.

Further reading

  • Holden, H.; Øksendal, B.; Ubøe, J.; Zhang, T. (2010). Stochastic Partial Differential Equations: A Modeling, White Noise Functional Approach. Universitext (2nd ed.). New York: Springer. doi:10.1007/978-0-387-89488-1. ISBN 978-0-387-89487-4.

External links

  • "A Minicourse on Stochastic Partial Differential Equations" (PDF). 2006.
  • Hairer, Martin (2009). "An Introduction to Stochastic PDEs". arXiv:0907.4178. Cite journal requires |journal= (help)