Multiple Arithmetic in Dynamic System Simulation

Václav Šátek, J. Kunovsky, J. Petrek
{"title":"Multiple Arithmetic in Dynamic System Simulation","authors":"Václav Šátek, J. Kunovsky, J. Petrek","doi":"10.1109/UKSIM.2008.46","DOIUrl":null,"url":null,"abstract":"A very interesting and promising numerical method of solving systems of ordinary differential equations based on Taylor series has appeared. The potential of the Taylor series has been exposed by many practical experiments and a way of detection and solution of large systems of ordinary differential equations has been found. Generally speaking, a stiff system contains several components, some of them are heavily suppressed while the rest remain almost unchanged. This feature forces the used method to choose an extremely small integration step and the progress of the computation may become very slow. There are many (implicit) methods for solving stiff systems of ODE’s, from the most simple such as implicit Euler method to more sophisticated (implicit Runge-Kutta methods) and finally the general linear methods. Usually a quite complicated auxiliary system of equations has to be solved in each step. These facts lead to immense amount of work to be done in each step of the computation. These are the reasons why one has to think twice before using the stiff solver and to decide between the stiff and non-stiff solver.","PeriodicalId":22356,"journal":{"name":"Tenth International Conference on Computer Modeling and Simulation (uksim 2008)","volume":"1 1","pages":"597-598"},"PeriodicalIF":0.0000,"publicationDate":"2008-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tenth International Conference on Computer Modeling and Simulation (uksim 2008)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UKSIM.2008.46","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

A very interesting and promising numerical method of solving systems of ordinary differential equations based on Taylor series has appeared. The potential of the Taylor series has been exposed by many practical experiments and a way of detection and solution of large systems of ordinary differential equations has been found. Generally speaking, a stiff system contains several components, some of them are heavily suppressed while the rest remain almost unchanged. This feature forces the used method to choose an extremely small integration step and the progress of the computation may become very slow. There are many (implicit) methods for solving stiff systems of ODE’s, from the most simple such as implicit Euler method to more sophisticated (implicit Runge-Kutta methods) and finally the general linear methods. Usually a quite complicated auxiliary system of equations has to be solved in each step. These facts lead to immense amount of work to be done in each step of the computation. These are the reasons why one has to think twice before using the stiff solver and to decide between the stiff and non-stiff solver.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
动态系统仿真中的多种算法
一种基于泰勒级数的求解常微分方程组的非常有趣和有前途的数值方法出现了。许多实际实验揭示了泰勒级数的潜力,并找到了一种检测和求解大型常微分方程组的方法。一般来说,一个刚性系统包含几个组成部分,其中一些被严重抑制,而其余的几乎保持不变。这一特点迫使所使用的方法选择极小的积分步长,计算的进度可能变得非常缓慢。求解ODE的刚性系统有许多(隐式)方法,从最简单的隐式欧拉法到更复杂的(隐式龙格-库塔法),最后是一般的线性方法。通常每一步都要求解一个相当复杂的辅助方程组。这些事实导致在计算的每一步都要做大量的工作。这就是为什么在使用刚性求解器之前必须三思而后行,并在刚性和非刚性求解器之间做出决定的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
ICCMS 2022: The 14th International Conference on Computer Modeling and Simulation, Chongqing, China, June 24 - 26, 2022 ICCMS 2021: The 13th International Conference on Computer Modeling and Simulation, Melbourne, VIC, Australia, June 25 - 27, 2021 Wheelchair Navigation System using Node Combination Technique for People with Motor Disabilities Reaeration and Dispersion Coefficients Prediction for a River Flow by Implementing Machine Learning Algorithm on MIKE Dataset ICCMS '20: The 12th International Conference on Computer Modeling and Simulation, Brisbane, QLD, Australia, June 22-24, 2020
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1