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nobuy
CLCO1
LanguageENG
PublishYear2015
publishCompany Wiley
EISBN 9781119018452
PISBN 9781119018445
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  • Contents
Introduction to Computation and Modeling for Differential Equations, Second Edition features the essential principles and applications to problem solving across disciplines such as engineering, physics, and chemistry. The Second Edition integrates the science of solving differential equations with mathematical, numerical, and programming tools, specifically with methods involving ordinary differential equations; numerical methods for initial value problems (IVPs); numerical methods for boundary value problems (BVPs); partial differential equations (PDEs); numerical methods for parabolic, elliptic, and hyperbolic PDEs; mathematical modeling with differential equations; numerical solutions; and finite difference and finite element methods.The author features a unique "Five-M" approach: Modeling, Mathematics, Methods, MATLAB, and Multiphysics, which facilitates a thorough understanding of how models are created and preprocessed mathematically with scaling, classification, and approximation and also demonstrates how a problem is solved numerically using the appropriate mathematical methods. With numerous real-world examples to aid readers in the visualization of the solutions, Introduction to Computation and Modeling for Differential Equations, Second Edition includes: New sections on topics such as variational formulation, the finite element method, examples of discretization, Ansatz methods for parabolic PDEs and elliptic PDEs, Galerkin's method for the model problem, and finite volume methods Numerous practical examples with applications in mechanics, fluid dynamics, solid mechanics, chemical engineering, electromagnetic field theory, and control theory, all of which are solved with computer programs MATLAB and Comsol Multiphysics Additional exercises that introduce new methods, projects, and problems to further illustrate possible applications A related website with select solutions to the exercises, as well as the MATLAB data sets for IVPs and BVPs
    Collected by
    • Princeton University
    • Yale University
    • University of Oxford
    • Stanford University
    • National Library of China
    • Jinan University
    • UCB

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