Numerical Matalb

This book offers an in-depth verification of numerical solutions for differential equations modeling heat transfer phenomena, where the smoothed particle hydrodynamics (SPH) method is used to discretize the mathematical models. Techniques described in this book aim to speed up the convergence of numerical solutions and increase their accuracy by significantly reducing the discretization error.

As suggested by the title of this book Numerical Toolbox for Verified Computing, we present an extensive set of sophisticated tools to solve basic numerical problems with a verification of the results. We use the features of the scientific computer language PASCAL-XSC to offer modules that can be combined by the reader to his/her individual needs. Our overriding concern is reliability - the automatic verification of the result a computer returns for a given problem. All algorithms we present are influenced by this central concern.

A Practical Approach to Understand the Numerical Methods

In recent years, mathematical models, numerical methods and data analysis have been paid more attention. After the finite-element method, the meshless method is another effective tool for solving science and enginnering problems. Numerical methods, such as the finite-element method, boundary element method and meshless method, have played important roles in numerical simulations of complicated problems in the science, engineering and society fields. Various numerical methods are presented to solve problems in different fields, and their corresponding computational efficiency, accuracy and convergence are also studied. With the development of big data, numerical simulation based on data analysis or big data will be an important direction for science and engineering computation. Deep learning is another new and effective approach to analyzing the properties of new materials.

This book gathers selected, peer-reviewed contributions presented at the Fifth International Conference on Numerical Analysis and Optimization (NAO-V), which was held at Sultan Qaboos University, Oman, on January 6-9, 2020. Each chapter reports on developments in key fields, such as numerical analysis, numerical optimization, numerical linear algebra, numerical differential equations, optimal control, approximation theory, applied mathematics, derivative-free optimization methods, programming models, and challenging applications that frequently arise in statistics, econometrics, finance, physics, medicine, biology, engineering and industry.

A concise introduction to numerical methodsand the mathematical framework neededto understand their performance
Numerical Solution of Ordinary Differential Equations presents a complete and easy-to-follow introduction to classical topics in the numerical solution of ordinary differential equations. The book's approach not only explains the presented mathematics, but also helps readers understand how these numerical methods are used to solve real-world problems.

The present book contains the 20 articles accepted and published in the Special Issue "Computational Methods and Applications for Numerical Analysis" of the MDPI "Mathematics" journal, which surround the theory, algorithms, programming, software, numerical simulation, and/or novel applications of computational methods to solve problems in engineering, science, and other disciplines related to computations. These topics include finite element methods, finite difference methods, meshless/meshfree methods, physics-informed neural networks, interpolation, approximation, optimization, numerical methods for ordinary/partial differential equations, etc. Their applications include crack propagation, acoustic analysis, elastodynamic analysis, free vibration analysis, structure and topology optimization, fractional equations, eigenvalue problems, inverse problems, etc.

All you need to know about numerical methods and their implementation is covered.

This text, covering a very large span of numerical methods and optimization, is primarily aimed at advanced undergraduate and graduate students. A background in calculus and linear algebra are the only mathematical requirements.

The book gives an informal introduction to mathematical and computational principles governing numerical analysis, as well as practical guidelines for using over 130 elaborate numerical analysis routines. It develops detailed formulas for both standard and rarely found algorithms, including many variants for linear and non-linear equation solvers, one- and two-dimensional splines of various kinds, numerical quadrature and cubature formulas of all known stable orders, and stable IVP and BVP solvers, even for stiff systems of differential equations.