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Stochastic Partial Differential Equations
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. Examples One of the most studied SPDEs is the stochastic heat equation, which may formally be written as : \partial_t u = \Delta u + \xi\;, where \Delta is the Laplacian and \xi denotes space-time white noise. Other examples also include stochastic versions of famous linear equations, such as the wave equation and the Schrödinger equation. 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 di ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Partial Differential Equations
In mathematics, a partial differential equation (PDE) is an equation which involves a multivariable function and one or more of its partial derivatives. The function is often thought of as an "unknown" that solves the equation, similar to how is thought of as an unknown number solving, e.g., an algebraic equation like . However, it is usually impossible to write down explicit formulae for solutions of partial differential equations. There is correspondingly a vast amount of modern mathematical and scientific research on methods to numerically approximate solutions of certain partial differential equations using computers. Partial differential equations also occupy a large sector of pure mathematical research, in which the usual questions are, broadly speaking, on the identification of general qualitative features of solutions of various partial differential equations, such as existence, uniqueness, regularity and stability. Among the many open questions are the existence an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Mild Solution
In mathematical analysis, a ''C''0-semigroup, also known as a strongly continuous one-parameter semigroup, is a generalization of the exponential function. Just as exponential functions provide solutions of scalar linear constant coefficient ordinary differential equations, strongly continuous semigroups provide solutions of linear constant coefficient ordinary differential equations in Banach spaces. Such differential equations in Banach spaces arise from e.g. delay differential equations and partial differential equations. Formally, a strongly continuous semigroup is a representation of the semigroup (R+, +) on some Banach space ''X'' that is continuous in the strong operator topology. Formal definition A strongly continuous semigroup on a Banach space X is a map T : \mathbb_+ \to L(X) (where L(X) is the space of bounded operators on X) such that # T(0) = I , (the identity operator on X) # \forall t,s \ge 0 : \ T(t + s) = T(t) T(s) # \forall x_0 \in X: \ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Stochastic Differential Equations
A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process, resulting in a solution which is also a stochastic process. SDEs have many applications throughout pure mathematics and are used to mathematical model, model various behaviours of stochastic models such as stock prices,Musiela, M., and Rutkowski, M. (2004), Martingale Methods in Financial Modelling, 2nd Edition, Springer Verlag, Berlin. random growth models or physical systems that are subjected to thermal fluctuations. SDEs have a random differential that is in the most basic case random white noise calculated as the distributional derivative of a Brownian motion or more generally a semimartingale. However, other types of random behaviour are possible, such as jump processes like Lévy processes or semimartingales with jumps. Stochastic differential equations are in general neither differential equations nor random differential equations. Random differe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Zakai Equation
In filtering theory the Zakai equation is a linear stochastic partial differential equation for the un-normalized density of a hidden state. In contrast, the Kushner equation gives a non-linear stochastic partial differential equation for the normalized density of the hidden state. In principle either approach allows one to estimate a quantity function (the state of a dynamical system) from noisy measurements, even when the system is non-linear (thus generalizing the earlier results of Wiener and Kalman for linear systems and solving a central problem in estimation theory). The application of this approach to a specific engineering situation may be problematic however, as these equations are quite complex. The Zakai equation is a bilinear stochastic partial differential equation. It was named after Moshe Zakai. __NOTOC__ Overview Assume the state of the system evolves according to :dx = f(x,t) dt + dw and a noisy measurement of the system state is available: :dz = h(x,t) d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Wick Product
In probability theory, the Wick product, named for Italian physicist Gian-Carlo Wick, is a particular way of defining an adjusted product of a set of random variables. In the lowest order product the adjustment corresponds to subtracting off the mean value, to leave a result whose mean is zero. For the higher-order products the adjustment involves subtracting off lower order (ordinary) products of the random variables, in a symmetric way, again leaving a result whose mean is zero. The Wick product is a polynomial function of the random variables, their expected values, and expected values of their products. The definition of the Wick product immediately leads to the Wick power of a single random variable, and this allows analogues of other functions of random variables to be defined on the basis of replacing the ordinary powers in a power series expansion by the Wick powers. The Wick powers of commonly-seen random variables can be expressed in terms of special functions such as ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Polynomial Chaos
Polynomial chaos (PC), also called polynomial chaos expansion (PCE) and Wiener chaos expansion, is a method for representing a random variable in terms of a polynomial function of other random variables. The polynomials are chosen to be orthogonal with respect to the joint probability distribution of these random variables. Note that despite its name, PCE has no immediate connections to chaos theory. The word "chaos" here should be understood as "random". PCE was first introduced in 1938 by Norbert Wiener using Hermite polynomials to model stochastic processes with Gaussian random variables. It was introduced to the physics and engineering community by R. Ghanem and P. D. Spanos in 1991 and generalized to other orthogonal polynomial families by D. Xiu and G. E. Karniadakis in 2002. Mathematically rigorous proofs of existence and convergence of generalized PCE were given by O. G. Ernst and coworkers in 2011. PCE has found widespread use in engineering and the applied sciences bec ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Malliavin Calculus
In probability theory and related fields, Malliavin calculus is a set of mathematical techniques and ideas that extend the mathematical field of calculus of variations from deterministic functions to stochastic processes. In particular, it allows the computation of derivatives of random variables. Malliavin calculus is also called the stochastic calculus of variations. P. Malliavin first initiated the calculus on infinite dimensional space. Then, the significant contributors such as S. Kusuoka, D. Stroock, J-M. Bismut, Shinzo Watanabe, I. Shigekawa, and so on finally completed the foundations. Malliavin calculus is named after Paul Malliavin whose ideas led to a proof that Hörmander's condition implies the existence and smoothness of a density for the solution of a stochastic differential equation; Hörmander's original proof was based on the theory of partial differential equations. The calculus has been applied to stochastic partial differential equations as well. The cal ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Kushner Equation
In filtering theory the Kushner equation (after Harold Kushner) is an equation for the conditional probability density of the state of a stochastic non-linear dynamical system, given noisy measurements of the state. It therefore provides the solution of the nonlinear filtering problem in estimation theory. The equation is sometimes referred to as the Stratonovich–Kushner Stratonovich, R.L. (1960). ''Conditional Markov Processes''. Theory of Probability and Its Applications, 5, pp. 156–178. (or Kushner–Stratonovich) equation. Overview Assume the state of the system evolves according to :dx = f(x,t) \, dt + \sigma\, dw and a noisy measurement of the system state is available: :dz = h(x,t) \, dt + \eta\, dv where ''w'', ''v'' are independent Wiener processes. Then the conditional probability density ''p''(''x'', ''t'') of the state at time ''t'' is given by the Kushner equation: :dp(x,t) = L (x,t)dt + p(x,t) \big(h(x,t)-E_t h(x,t) \big)^\top \eta^\eta^ \b ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Kardar–Parisi–Zhang Equation
In mathematics, the Kardar–Parisi–Zhang (KPZ) equation is a non-linear stochastic partial differential equation, introduced by Mehran Kardar, Giorgio Parisi, and Yi-Cheng Zhang in 1986. It describes the temporal change of a height field h(\vec x,t) with spatial coordinate \vec x and time coordinate t: : \frac = \nu \nabla^2 h + \frac \left(\nabla h\right)^2 + \eta(\vec x,t) \; . Here, \eta(\vec x,t) is white Gaussian noise with average \langle \eta(\vec x,t) \rangle = 0 and second moment \langle \eta(\vec x,t) \eta(\vec x',t') \rangle = 2D\delta^d(\vec x-\vec x')\delta(t-t'), \nu, \lambda, and D are parameters of the model, and d is the dimension. In one spatial dimension, the KPZ equation corresponds to a stochastic version of Burgers' equation with field u(x,t) via the substitution u=-\lambda\, \partial h/\partial x. Via the renormalization group, the KPZ equation is conjectured to be the field theory of many surface growth models, such as the Eden model, ball ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Brownian Surface
A Brownian surface is a Fractal landscape, fractal surface generated via a fractal elevation Function (mathematics), function. The Brownian surface is named after Brownian motion. Example For instance, in the three-dimensional case, where two variables ''X'' and ''Y'' are given as coordinates, the elevation function between any two points (''x''1, ''y''1) and (''x''2, ''y''2) can be set to have a mean or expected value that increases as the Euclidean vector, vector distance between (''x''1, ''y''1) and (''x''2, ''y''2). There are, however, many ways of defining the elevation function. For instance, the fractional Brownian motion variable may be used, or various rotation functions may be used to achieve more natural looking surfaces. Generation of fractional Brownian surfaces Efficient generation of fractional Brownian surfaces poses significant challenges. Since the Brownian surface represents a Gaussian process with a nonstationary covariance function, one c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Renormalization
Renormalization is a collection of techniques in quantum field theory, statistical field theory, and the theory of self-similar geometric structures, that is used to treat infinities arising in calculated quantities by altering values of these quantities to compensate for effects of their self-interactions. But even if no infinities arose in loop diagrams in quantum field theory, it could be shown that it would be necessary to renormalize the mass and fields appearing in the original Lagrangian. For example, an electron theory may begin by postulating an electron with an initial mass and charge. In quantum field theory a cloud of virtual particles, such as photons, positrons, and others surrounds and interacts with the initial electron. Accounting for the interactions of the surrounding particles (e.g. collisions at different energies) shows that the electron-system behaves as if it had a different mass and charge than initially postulated. Renormalization, in this example, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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C0-semigroup
In mathematical analysis, a ''C''0-semigroup, also known as a strongly continuous one-parameter semigroup, is a generalization of the exponential function. Just as exponential functions provide solutions of scalar linear constant coefficient ordinary differential equations, strongly continuous semigroups provide solutions of linear constant coefficient ordinary differential equations in Banach spaces. Such differential equations in Banach spaces arise from e.g. delay differential equations and partial differential equations. Formally, a strongly continuous semigroup is a representation of the semigroup (R+, +) on some Banach space ''X'' that is continuous in the strong operator topology. Formal definition A strongly continuous semigroup on a Banach space X is a map T : \mathbb_+ \to L(X) (where L(X) is the space of bounded operators on X) such that # T(0) = I , (the identity operator on X) # \forall t,s \ge 0 : \ T(t + s) = T(t) T(s) # \forall x_0 \in X: ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |