Stochastic Partial Differential Equation

Differential equations
Navier–Stokes differential equations used to simulate airflow around an obstruction.
Scope Natural sciences Engineering Astronomy Physics Chemistry Biology Geology Applied mathematics Continuum mechanics Chaos theory Dynamical systems Social sciences Economics Population dynamics
Classification
Types Ordinary Partial Differential-algebraic Integro-differential Fractional Linear Non-linear By variable type Dependent and independent variables Autonomous Complex Coupled / Decoupled Exact Homogeneous / Nonhomogeneous Features Order Operator Notation
Relation to processes Difference (discrete analogue) Stochastic Stochastic partial Delay
Solution
General topics Picard–Lindelöf theorem Wronskian Phase portrait Phase space Lyapunov / Asymptotic / Exponential stability Rate of convergence Series / Integral solutions Numerical integration Dirac delta function
Solution methods Inspection Method of characteristics Euler Exponential response formula Finite difference (Crank–Nicolson) Finite element Infinite element Finite volume Galerkin Petrov–Galerkin Integrating factor Integral transforms Perturbation theory Runge–Kutta Separation of variables Undetermined coefficients Variation of parameters
People Isaac Newton Gottfried Leibniz Leonhard Euler Émile Picard Józef Maria Hoene-Wroński Ernst Lindelöf Rudolf Lipschitz Augustin-Louis Cauchy John Crank Phyllis Nicolson Carl David Tolmé Runge Martin Kutta
v t e

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]

Examples

One of the most studied SPDEs is the stochastic heat equation,^[3] which may formally be written as

[math]\displaystyle{ \partial_t u = \Delta u + \xi\;, }[/math]

where [math]\displaystyle{ \Delta }[/math] is the Laplacian and [math]\displaystyle{ \xi }[/math] denotes space-time white noise. Other examples also include stochastic versions of famous linear equations, such as the wave equation^[4] and the Schrödinger equation.^[5]

Discussion

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.^[6]

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

[math]\displaystyle{ \partial_t u = \Delta u + P(u) + \xi, }[/math]

where [math]\displaystyle{ P }[/math] 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 in dimension larger than one, 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 Prato–Debussche trick which involved studying such non-linear equations as perturbations of linear ones.^[7] 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 SPDEs.^[8]

See also

• Brownian surface
• Kardar–Parisi–Zhang equation
• Kushner equation
• Malliavin calculus
• Polynomial chaos
• Wick product
• Zakai equation

References

Further reading

• Bain, A.; Crisan, D. (2009). Fundamentals of Stochastic Filtering. Stochastic Modelling and Applied Probability. 60. New York: Springer. ISBN 978-0387768953. 
• 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. 
• Lindgren, F.; Rue, H.; Lindström, J. (2011). "An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach". Journal of the Royal Statistical Society Series B: Statistical Methodology 73 (4): 423–498. doi:10.1111/j.1467-9868.2011.00777.x. ISSN 1369-7412. https://academic.oup.com/jrsssb/article/73/4/423/7034732. 
• Xiu, D. (2010). Numerical Methods for Stochastic Computations: A Spectral Method Approach. Princeton University Press. ISBN 978-0-691-14212-8. 

External links

• "A Minicourse on Stochastic Partial Differential Equations". 2006. https://web.math.rochester.edu/people/faculty/cmlr/Preprints/Utah-Summer-School.pdf. 
• Hairer, Martin (2009). "An Introduction to Stochastic PDEs". arXiv:0907.4178 [math.PR].

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