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Computer algebra derives normal forms of stochastic differential equations

机译:计算机代数导出随机微分方程的正规形式

摘要

[Abstract]: Modelling stochastic systems has many important applications. Normal form coordinate transforms are a powerful way to untangle interesting long term dynamics from undesirably detailed microscale dynamics. I aim to explore normal forms of stochastic differential equations when the dynamics has both slow modes and quickly decaying modes. The thrust is to derive normal forms useful for macroscopic modelling of detailed microscopic systems. Thus we not only must reduce the dimensionality of the dynamics, but also endeavour to remove all fast time processes. Sri Namachchivaya, Leng and Lin (1990­1 emphasise the importance of quadratic stochastic effects 'in order to capture the stochastic contributions of the stable modes to the drift terms of the critical modes.' I derive such important quadratic effects using the normal form coordinate transform to separate slow and fast modes. The results will help us accurately model multiscale stochastic systems.ud
机译:[摘要]:随机系统建模具有许多重要的应用。法线形式的坐标变换是从令人讨厌的详细微观动力学中解开有趣的长期动力学的有力方法。当动力学同时具有慢速模式和快速衰减模式时,我旨在探索随机微分方程的范式。重点是得出可用于详细微观系统的宏观建模的范式。因此,我们不仅必须降低动力学的维数,而且还要努力消除所有快速过程。 Sri Namachchivaya,Leng和Lin(19901)强调了二次随机效应的重要性,“以便捕获稳定模态对临界模态的漂移项的随机贡献。”我使用正态形式坐标变换得出了如此重要的二次效应。分离慢速模式和快速模式。结果将有助于我们准确地对多尺度随机系统进行建模。

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  • 作者

    Roberts A. J.;

  • 作者单位
  • 年度 2007
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  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
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