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As more applied science researchers are attempting to use Stochastic Differential Equations (SDEs) as well as Stochastic Partial Differential Equations (SPDEs) in their modeling, especially when involving Fractional Brownian Motion (fBM), one common issue appears: an exact solution cannot always be found. For cases involving SPDEs, exact solutions commonly do not exist and approximation schemes for their solution are typically still in development. Therefore, in this paper, we test various Numerical methods in solving SDEs and SPDEs with standard BM that have non-linear coeffi cients. In addition we extend our results to problems with fBM.
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Tom
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3--12
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Bibliogr. 15 poz., rys., tab., wykr.
Twórcy
Bibliografia
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- [5] Kloeden P., and Platen E. Numerical Solution of Stochastic Diff erential Equations. Springer 1992.
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- [11] Picchini U. SDE Toolbox: Simulation and Estimation of Stochastic Diff erential Equations with MATLAB, http://sdetoolbox.sourceforge.net. 2007.
- [12] Jentzen A., and Kloeden P.E. “The Numerical Approximation of Stochastic Differential Equations”. Milan J. Math 77 (2009): 205–244.
- [13] Davie A.M., and Gaines J.G. “Convergence of Numerical Schemes for the Solution of Parabolic Stochastic Partial Differential Equations”. Mathematics of Computation 70 (2000): 121–134.
- [14] Gyöngy I. “Lattice Approximations for Stochastic Quasi-Linear Parabolic Partial Differential Equations Driven by Space-Time White Noise I”. Pot. Anal. 9 (1998): 1–25.
- [15] Gyöngy I. “Lattice Approximations for Stochastic Quasi-Linear Parabolic Partial Differential Equations Driven by Space-Time White Noise II”. Pot. Anal. 11 (1999): 1–37.
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Bibliografia
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bwmeta1.element.baztech-367cbf42-e60f-4c0e-9090-049750c7e2fa