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Advanced Numerical and Semi-Analytical Methods for Differential Equations最新文献

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Basic Numerical Methods 基本数值方法
Pub Date : 2019-04-05 DOI: 10.1002/9781119423461.ch1
R. Masenge
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引用次数: 0
Differential Quadrature Method 微分求积法
Pub Date : 2019-04-05 DOI: 10.1016/B978-0-12-803081-3.00001-2
Xinwei Wang
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引用次数: 4
Exp-Function Method Exp-Function方法
Pub Date : 2019-04-05 DOI: 10.1002/9781119423461.ch10
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引用次数: 1
Hybrid Methods 混合的方法
Pub Date : 2019-04-05 DOI: 10.1002/9781119423461.ch17
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引用次数: 0
Boundary Element Method 边界元法
Pub Date : 2019-04-05 DOI: 10.1002/9781119423461.ch8
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引用次数: 0
Adomian Decomposition Method 阿多米亚分解法
Pub Date : 2019-04-05 DOI: 10.1002/9781119423461.ch11
S. Chakraverty, N. Mahato, P. Karunakar, T. D. Rao
The Adomian decomposition method (ADM) is an efficient semi‐analytical technique used for solving linear and nonlinear differential equations. It permits us to handle both nonlinear initial value problems (IVPs) and boundary value problems. The solution technique of this method is mainly based on decomposing the solution of nonlinear operator equation to a series of functions. Each presented term of the obtained series is developed from a polynomial generated in the expansion of an analytic function into a power series. This chapter presents procedures for solving linear as well as nonlinear ordinary/partial differential equations by the ADM along with example problems for clear understanding. It also presents linear and nonlinear IVPs for clear understanding of the ADM for ordinary differential equations. ADM transforms system of partial differential equations into a set of recursive relation that can easily be handled. To understand the method, one can consider the system of linear partial differential equations.
Adomian分解法(ADM)是一种求解线性和非线性微分方程的有效半解析方法。它允许我们处理非线性初值问题(IVPs)和边值问题。该方法的求解技术主要是将非线性算子方程的解分解为一系列函数。所得到的级数的每一项都是从解析函数展开成幂级数时产生的多项式发展而来的。本章介绍了用ADM求解线性和非线性常微分方程/偏微分方程的过程,并附有示例问题,以便清楚地理解。它还提出了线性和非线性ivp,以便清楚地理解常微分方程的ADM。ADM将偏微分方程组转化为一组易于处理的递归关系。为了理解这种方法,我们可以考虑线性偏微分方程组。
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引用次数: 0
Differential Equations with Interval Uncertainty 具有区间不确定性的微分方程
Pub Date : 2019-04-05 DOI: 10.1002/9781119423461.ch19
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引用次数: 0
Weighted Residual Methods 加权残差法
Pub Date : 2018-10-03 DOI: 10.1007/978-3-642-58108-3_4
C. Fletcher
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引用次数: 3
Variational Iteration Method 变分迭代法
Pub Date : 2012-06-09 DOI: 10.1002/9781119423461.ch13
B. Batiha
Differential equations are encountered in various fields such as physics, chemistry, biology, mathematics and engineering. Most nonlinear models of real-life problems are still very difficult to solve either numerically or theoretically. Many unrealistic assumptions have to be made to make nonlinear models solvable. There has recently been much attention devoted to the search for better and more efficient solution methods for determining a solution, approximate or exact, analytical or numerical, to nonlinear models. Finding exact/approximate solutions of these nonlinear equations are interesting and important. One of these methods is variational iteration method (VIM), which has been proposed by Ji-Huan He in 1997 based on the general Lagrange’s multiplier method. The main feature of the method is that the solution of the linearized problem is used as the initial approximation for the linear and nonlinear problems. Then a more highly precise approximation at some special point can be obtained. This approximation converges rapidly to an accurate solution. VIM is very powerful and efficient in finding analytical as well as numerical solutions for a wide class of differential equation
微分方程在物理、化学、生物、数学和工程等各个领域都有应用。大多数现实问题的非线性模型无论是在数值上还是理论上都很难解决。为了使非线性模型可解,必须做出许多不切实际的假设。最近,人们非常关注寻找更好和更有效的求解方法来确定非线性模型的近似解或精确解,解析解或数值解。寻找这些非线性方程的精确/近似解是有趣和重要的。其中一种方法是变分迭代法(VIM),该方法是何继欢于1997年在一般拉格朗日乘子法的基础上提出的。该方法的主要特点是将线性化问题的解作为线性和非线性问题的初始逼近。然后,在某一特殊点上可以得到更精确的近似值。这个近似很快收敛到一个精确的解。VIM在寻找一类广泛的微分方程的解析解和数值解方面非常强大和有效
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引用次数: 13
期刊
Advanced Numerical and Semi-Analytical Methods for Differential Equations
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