Results 261 to 270 of about 12,854 (309)
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1998
In this chapter we first present some random fixed point theorems for random operators. These results rely on classical continuation methods; in particular on the idea of an essential map. In section 11.3 our fixed point theory will then be applied to obtain a general existence principle for stochastic integral equations of Volterra type.
Donal O’Regan, Maria Meehan
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In this chapter we first present some random fixed point theorems for random operators. These results rely on classical continuation methods; in particular on the idea of an essential map. In section 11.3 our fixed point theory will then be applied to obtain a general existence principle for stochastic integral equations of Volterra type.
Donal O’Regan, Maria Meehan
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Stochastic Integrals and Stochastic Differential Equations
1985Roughly speaking, stochastic differential equations are differential equations driven by Gaussian white noise. Here, we are using the term “stochastic differential equations” in a restricted sense and not merely to denote differential equations with some probabilistic aspects. The importance of.
Eugene Wong, Bruce Hajek
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Stochastic product integration and stochastic equations
1987A standard method in deterministic product (or multiplicative) integration for integrating measures (or w.r.t measures) is to exploit Radon-Nikodym property. This technique does not extend to stochastic product integration w.r.t semimartingales. We introduce in this article a multiplicative operator functional (MOF) method to define stochastic product ...
L. Hazareesingh, D. Kannan
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On Solutions of Integral Equations with an Extended Stochastic Integral
Theory of Probability & Its Applications, 1996The article is devoted to the integral equations of the second kind with the extended (Skorokhod) stochastic integral. It is proved, that in some cases the generalized solution in the Hida sense can be considered as a usual random process without finite second moment.
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Numerical Integration of Stochastic Differential Equations
Bell System Technical Journal, 1979In a previous paper, a method was presented to integrate numerically nonlinear stochastic differential equations (SDEs) with additive, Gaussian, white noise. The method, a generalization of the Range Kutta algorithm, extrapolates from one point to the next applying functional evaluations at stochastically determined points.
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INTEGRATION OF STOCHASTIC DIFFERENTIAL EQUATIONS ON A COMPUTER
International Journal of Modern Physics C, 2002A brief introduction to the simulation of stochastic differential equations is presented. Algorithms to simulate rare fluctuations, a topic of interest in the light of recent theoretical work on optimal paths are studied. Problems connected to the treatment of the boundaries and correlated noise will also be discussed.
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A New Representation for Stochastic Integrals and Equations
SIAM Journal on Control, 1966zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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ISDEP: Integrator of stochastic differential equations for plasmas
Computer Physics Communications, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jose Luis Velasco +5 more
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Stochastic multisymplectic integrator for stochastic KdV equation
AIP Conference Proceedings, 2012In this paper we investigate the stochastic multisymplectic methods to solve the stochastic partial differential equation. The stochastic KdV equations are considered. Besides conserving the multi-symplectic structure of original equation, the stochastic multi-symplectic methods are also investigated for the conservation of various conservation laws ...
Shanshan Jiang, Lijin Wang, Jialin Hong
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On set-valued stochastic integrals and fuzzy stochastic equations
Fuzzy Sets and Systems, 2011The author establishes the notion of a set-valued trajectory stochastic integral in a semimartingale framework. The notion of this set-valued stochastic integral arises in a natural way by the corresponding notion of the decomposable hull of a map with respect to a semimartingale and a filtration. Formal stochastic equations are studied with respect to
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