Titlebook: Applied Scientific Computing; With Python Peter R. Turner,Thomas Arildsen,Kathleen Kavanagh Textbook 2018 Springer International Publishing

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Motivation and Background,eling and python programming. Any computational solution has errors inherent in the process. In this introduction we see that simplistic approaches to approximating a function through a power series are often not practical, but that a careful restructuring of the problem can overcome that.

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Number Representations and Errors, and therefore to estimate them, or even mitigate them. Numerical processes are themselves finite. The finiteness of processes gives rise to truncation errors, for example resulting from restricting the number of terms in a series that we compute. In other settings it might be a spatial, or temporal

Expiration

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Linear Equations,uce the two fundamental approaches: Jacobi and Gauss-Seidel iterations. Next we turn to (linear) least squares approximation. This refers to the problem of finding the “best” fit to specified data using a linear combination of simpler functions such as the terms of a polynomial. The final topic of t

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Differential Equations,two can be used to advantage as a predictor-corrector pair. Treating the independent and dependent variables as vector quantities allows systems of differential equations to be approached using these same methods. Higher order differential equations can also be recast as systems of first-order equat

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Selection of Static Supply Portfolioquences of the use of materials and structures by means of continuous functions. A material is a point (element), and a structure is a body. The structure may be considered as a partly ordered set of material elements (points) filling a structure (body). The cube is often used as an element.