✦ LIBER ✦

Numerical Analysis of Systems of Ordinary and Stochastic Differential Equationsby S. S. Artemiev; T. A. Averina

✍ Scribed by Review by: Kevin Burrage

Book ID: 124941145
Publisher: Society for Industrial and Applied Mathematics
Year: 1999
Tongue: English
Weight: 310 KB
Volume: 41
Category: Article
ISSN: 0036-1445
DOI: 10.2307/2653212

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Numerical analysis of stochastic oscilla

Numerical analysis of stochastic oscillators on supercomputers

✍ S. S. Artemiev; A. A. Ivanov; V. D. Korneev 📂 Article 📅 2012 🏛 Pleiades Publishing 🌐 English ⚖ 958 KB

Numerical solution of stochastic differe

Numerical solution of stochastic differential equations with random structure on supercomputers

✍ Artemiev, S. S.; Korneev, V. D.; Yakunin, M. A. 📂 Article 📅 2013 🏛 Pleiades Publishing 🌐 English ⚖ 576 KB

Towards a Systematic Linear Stability An

Towards a Systematic Linear Stability Analysis of Numerical Methods for Systems of Stochastic Differential Equations

✍ Buckwar, Evelyn; Kelly, Cónall 📂 Article 📅 2010 🏛 Society for Industrial and Applied Mathematics 🌐 English ⚖ 626 KB

On the Numerical Integration of Ordinary

On the Numerical Integration of Ordinary Differential Equations by Processed Methods

✍ Blanes, S.; Casas, F.; Murua, A. 📂 Article 📅 2004 🏛 Society for Industrial and Applied Mathematics 🌐 English ⚖ 297 KB

Analysis of the accuracy of Monte Carlo

Analysis of the accuracy of Monte Carlo methods for boundary-value problems using a probabilistic representation

✍ T. A. Averina; S. S. Artemiev 📂 Article 📅 2008 🏛 Pleiades Publishing 🌐 English ⚖ 779 KB

A limit theorem for stochastic two-point

A limit theorem for stochastic two-point boundary-value problems of ordinary differential equations

✍ B. S. White; J. N. Franklin 📂 Article 📅 1979 🏛 John Wiley and Sons 🌐 English ⚖ 624 KB

In this paper, we prove a limit theorem applicable to the asymptotic solution of stochastic two-point boundary-value and eigenvalue problems of a wide class of non-linear ordinary differential equations. Let E, 0 < E << 1, be a small parameter, and y ( s ) for 0 5 s <m a stochastic process. Then ~(