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rajasekar s numerical methods classical advanced topics 2024

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Textbook in PDF format This book presents a pedagogical treatment of a wide range of numerical methods to suit the needs of undergraduate and postgraduate students, and teachers and researchers in physics, mathematics, and engineering. For each method, the derivation of the formula/algorithm, error analysis, case studies, applications in science and engineering and the special features are covered. A detailed presentation of solving time-dependent Schrödinger equation and nonlinear wave equations, along with the Monte Carlo techniques (to mention a few) will aid in students’ understanding of several physical phenomena including tunnelling, elastic collision of nonlinear waves, electronic distribution in atoms, and diffusion of neutrons through simulation study. The book covers advanced topics such as symplectic integrators and random number generators for desired distributions and Monte Carlo techniques, which are usually overlooked in other numerical methods textbooks. Interesting updates on classical topics include: curve fitting to a sigmoid and Gaussian functions and product of certain two functions, solving of differential equations in the presence of noise, and solving the time-independent Schrödinger equation. Solutions are presented in the forms of tables and graphs to provide visual aid and encourage a deeper comprehension of the topic. The step-by-step computations presented for most of the problems can be verifiable using a scientific calculator and is therefore appropriate for classroom teaching. The readers of the book will benefit from acquiring an acquittance, knowledge, experience and realization of significance of the numerical methods covered, their applicability to physical and engineering problems and the advantages of applying numerical methods over theoretical methods for specific problems. Preface. About the Author. Preliminaries. Solutions of Polynomial and Reciprocal Equations. Solution of General Nonlinear Equations. Solution of Linear Systems AX = B Curve Fitting. Interpolation and Extrapolation. Eigenvalues and Eigenvectors. Numerical Diﬀerentiation. Numerical Minimization of Functions. Numerical Integration. Ordinary Diﬀerential Equations – Initial-Value Problems. Symplectic Integrators for Hamiltonian Systems. Ordinary Diﬀerential Equations – Boundary-Value Problems. Linear Partial Diﬀerential Equations. Nonlinear Partial Diﬀerential Equations. Fractional Order Ordinary Diﬀerential Equations. Fractional Order Partial Diﬀerential Equations. Fourier Analysis and Power Spectrum. Random Numbers. Monte Carlo Technique. Answers to Some Selected Problems. Index

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rajasekar s numerical methods classical advanced topics 2024

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