# Numerical methods for second-order stochastic differential equations

Burrage, Kevin, Lenane, Ian, & Lythe, Grant
(2007)
Numerical methods for second-order stochastic differential equations.
*SIAM Journal on Scientific Computing*, *29*(1), pp. 245-264.

## Abstract

We seek numerical methods for second‐order stochastic differential equations that reproduce the stationary density accurately for all values of damping. A complete analysis is possible for scalar linear second‐order equations (damped harmonic oscillators with additive noise), where the statistics are Gaussian and can be calculated exactly in the continuous‐time and discrete‐time cases. A matrix equation is given for the stationary variances and correlation for methods using one Gaussian random variable per timestep. The only Runge–Kutta method with a nonsingular tableau matrix that gives the exact steady state density for all values of damping is the implicit midpoint rule. Numerical experiments, comparing the implicit midpoint rule with Heun and leapfrog methods on nonlinear equations with additive or multiplicative noise, produce behavior similar to the linear case.

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ID Code: | 44500 |
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Item Type: | Journal Article |

Refereed: | Yes |

Keywords: | damped harmonic oscillators with noise, stationary distribution, stochastic Runge¿Kutta methods, implicit midpoint rule, multiplicative noise |

DOI: | 10.1137/050646032 |

ISSN: | 1064-8275 |

Subjects: | Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > APPLIED MATHEMATICS (010200) Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > NUMERICAL AND COMPUTATIONAL MATHEMATICS (010300) Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTATION THEORY AND MATHEMATICS (080200) |

Divisions: | Past > QUT Faculties & Divisions > Faculty of Science and Technology |

Deposited On: | 24 Aug 2011 22:12 |

Last Modified: | 29 Feb 2012 14:35 |

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