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- Kevin Burrage
- Numerical analysis and scientific computation
- 1995

What do you do to start reading parallel and sequential methods for ordinary differential equations? Searching the book that you love to read first or find an interesting book that will make you want to read? Everybody has difference with their reason of reading a book. Actuary, reading habit must be from earlier. Many people may be love to read, but not a… (More)

- Manuel Barrio, Kevin Burrage, André Leier, Tianhai Tian
- PLoS Computational Biology
- 2006

Discrete stochastic simulations are a powerful tool for understanding the dynamics of chemical kinetics when there are small-to-moderate numbers of certain molecular species. In this paper we introduce delays into the stochastic simulation algorithm, thus mimicking delays associated with transcription and translation. We then show that this process may well… (More)

- Tianhai Tian, Kevin Burrage
- The Journal of chemical physics
- 2004

This paper discusses efficient simulation methods for stochastic chemical kinetics. Based on the tau-leap and midpoint tau-leap methods of Gillespie [D. T. Gillespie, J. Chem. Phys. 115, 1716 (2001)], binomial random variables are used in these leap methods rather than Poisson random variables. The motivation for this approach is to improve the efficiency… (More)

- Kevin Burrage, Pamela M. Burrage
- SIAM J. Numerical Analysis
- 2000

In this paper, general order conditions and a global convergence proof are given for stochastic Runge–Kutta methods applied to stochastic ordinary differential equations (SODEs) of Stratonovich type. This work generalizes the ideas of B-series as applied to deterministic ordinary differential equations (ODEs) to the stochastic case and allows a completely… (More)

- Shev MacNamara, Alberto Maria Bersani, Kevin Burrage, Roger B. Sidje
- The Journal of chemical physics
- 2008

Recently the application of the quasi-steady-state approximation (QSSA) to the stochastic simulation algorithm (SSA) was suggested for the purpose of speeding up stochastic simulations of chemical systems that involve both relatively fast and slow chemical reactions [Rao and Arkin, J. Chem. Phys. 118, 4999 (2003)] and further work has led to the nested and… (More)

This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations (SDEs). We give a brief survey of the area focusing on a number of application areas where approximations to strong solutions are important, with a particular focus on computational… (More)

- Roger B. Sidje, Kevin Burrage, Shev MacNamara
- SIAM J. Scientific Computing
- 2007

- Pamela M. Burrage, Kevin Burrage
- SIAM J. Scientific Computing
- 2003

Stochastic differential equations (SDEs) arise from physical systems where the parameters describing the system can only be estimated or are subject to noise. Much work has been done recently on developing higher order Runge–Kutta methods for solving SDEs numerically. Fixed stepsize implementations of numerical methods have limitations when, for example,… (More)

- Jørgen Sand, Kevin Burrage
- SIAM J. Scientific Computing
- 1998

A Jacobi waveform relaxation method for solving initial value problems for ordinary diierential equations (ODEs) is presented. In each window the method uses a technique called dynamic tting and a pair of continuous Runge-Kutta formulas to produce the initial waveform, after which a xed number of waveform iterates are computed. The reliability and eecacy of… (More)

In recent years considerable attention has been paid to the numerical solution of stochastic ordinary diierential equations (SODEs), as SODEs are often more appropriate than their deterministic counterparts in many modelling situations. However, unlike the deterministic case numerical methods for SODEs are considerably less sophisticated due to the diiculty… (More)