Mathematical Sciences: Numerical Analysis for Time-Dependent Differential Equations

数学科学：时态微分方程的数值分析

• 批准号: 9504879
• 负责人: Gene Golub
• 金额: $ 23.83万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-06-01 至 2000-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9504879&HistoricalAwards=false
• 关键词: Mathematical; Sciences; Numerical; Analysis; Time

Stuart The investigator studies the numerical approximation of problems that change in time and are modeled by ordinary or partial differential evolution equations. Two fundamental issues arise in this context. The first is, given a numerical simulation of a problem that evolves in time, how should the data obtained be interpreted: under what circumstances, and in what sense, is the computed data close to the data generated by the underlying differential equation? The first component of the project is concerned with studying this question in the context of the complicated algorithms used by software codes; this is primarily an analytical study requiring an understanding of the discontinuous dynamical systems generated by these algorithms. The second fundamental issue is how to effectively design numerical methods that enable given questions about the equations to be answered efficiently and in a way that yields new insights. The second component of the project is concerned with studying this question in the context of dissipative partial differential equations and, in particular, developing computational techniques for connecting orbits; equations such as the Ginzburg-Landau equation and Kuramoto-Sivashinsky equation are studied. Many problems in science and engineering can be edfectively modeled by means of differential equations of evolution. In practice these equations cannot be solved exactly and computational approximations must be sought. Two important issues arise in this context: (i) ``what is the relationship between computer generated data and the real problem we wish to understand'' and (ii) `` how can efficient algorithms be developed to obtain the type of information that sheds most light on the questions of interest.'' These two issues are addressed in this project in a variety of different contexts. For the first a very broad class of algorithms is studied with potential impact on many areas of science and engineering, including molecular dynamics simulations, planetary interaction simulations and other interesting evolution problems. For the second a number of particular problems are studied that arise from, for example, models of transition to turbulence in fluids and models of complicated combustion processes.

斯图尔特 研究人员研究随时间变化的问题的数值近似，并通过普通或偏微分演化方程建模。 在这方面出现了两个基本问题。 第一个问题是，给定一个随时间演变的问题的数值模拟，应该如何解释所获得的数据：在什么情况下，在什么意义上，计算出的数据接近基本微分方程产生的数据？该项目的第一个组成部分是关于研究这个问题的背景下使用的软件代码的复杂算法，这主要是一个分析研究，需要了解这些算法产生的不连续动力系统。 第二个基本问题是如何有效地设计数值方法，使给定的问题的方程得到有效的回答，并在某种程度上产生新的见解。 该项目的第二个组成部分涉及在耗散偏微分方程的范围内研究这一问题，特别是发展连接轨道的计算技术;研究了诸如金斯堡-朗道方程和Kuramoto-Sivashinsky方程等方程。 科学和工程中的许多问题都可以用发展微分方程有效地模拟。 在实践中，这些方程不能精确求解，必须寻求计算近似。 在这方面出现了两个重要的问题：（一）“计算机生成的数据与我们希望了解的真实的问题之间的关系是什么”和（二）"如何才能开发有效的算法来获得对感兴趣的问题最有启发的信息类型。这两个问题在本项目中在各种不同的背景下得到了解决。 对于第一个非常广泛的一类算法的研究与科学和工程的许多领域的潜在影响，包括分子动力学模拟，行星相互作用模拟和其他有趣的进化问题。 对于第二个一些特殊的问题进行了研究，所产生的，例如，模型的过渡到湍流的流体和模型的复杂燃烧过程。