NUMERICAL METHODS FOR ENGINEERS

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The unique compendium is an introductory reference to learn the most popular numerical methods cohesively. The text focuses on practical applications rather than on abstract and heavy analytical concepts. The key elements of the numerical methods are Taylor series and linear algebra. Based on the authors' years of experience, most materials on the text are tied to those elements in a unified manner. The useful reference manual benefits professionals, researchers, academics, senior undergraduate and graduate students in chemical engineering, civil engineering, mechanical engineering and aerospace engineering. Sample Chapter(s) Preface Chapter 1: Fundamentals: Taylor Series Contents: Fundamentals: Taylor Series Linear Algebra Interpolation and Fitting Nonlinear Equations Numerical Differentiation and Integration Ordinary Differential Equations: Initial Value Problems Ordinary Differential Equations: Boundary Value Problems Partial Differential Equations Diffusion Equation (Parabolic Equation) Laplace and Poisson Equations (Elliptic Equations) Advection and Avection–Diffusion Equations Wave Equations Request Inspection Copy Readership: Researchers, professionals, academics, undergraduate and graduate students in chemical engineering, civil engineering and mechanical engineering.

Applied Numerical Methods with Python for Engineers and Scientists (1版)

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【簡介】 本書介紹 　　雖然MATLAB提供高品質與強大的運算平台，可是，採購的價格卻讓許多大學望而卻步，對此問題，作者 Chapra提供了另一種選擇方案，即「透過 Python 來學習數值方法」；本書是為工學院與理學院相關系所學生精心設計的核心教材，除了扎實的理論說明，在許多章節裡還附上許多工程應用的案例，從背景陳述到詳細的解題過程，協助學生能深刻的理解與應用；內容文筆與深度都十分容易閱讀，適合大學老師採用與指定為「數值方法」課程的學習課本。 【目錄】 Table of Contents PART ONE Modeling, Computers, and Error Analysis 1 Mathematical Modeling, Numerical Methods, and Problem Solving 2 Python Fundamentals 3 Programming in Python 4 Roundoff and Truncation Errors PART TWO Roots and Optimization 5 Roots: Bracketing Methods 6 Roots: Open Methods 7 Optimization PART THREE Linear Systems 8 Linear Algebraic Equations and Matrices 9 Gauss Elimination 10 LU Factorization 11 Matrix Inverse and Condition 12 Iterative Methods 13 Eigenvalues PART FOUR Curve Fitting 14 Straight-Line Linear Regression 15 General Linear and Nonlinear Regression 16 Fourier Analysis 17 Polynomial Interpolation 18 Splines and Piecewise Interpolation PART FIVE Integration and Differentiation 19 Numerical Integration Formulas 20 Numerical Integration of Functions 21 Numerical Differentiation PART SIX Ordinary Differential Equations 22 Initial-Value Problems 23 Adaptive Methods and Stiff Systems 24 Boundary-Value Problems