Tao Pang – författare

Thoroughly revised for its second edition, this advanced textbook provides an introduction to the basic methods of computational physics, and an overview of progress in several areas of scientific computing by relying on free software available from CERN. The book begins by dealing with basic computational tools and routines, covering approximating functions, differential equations, spectral analysis, and matrix operations. Important concepts are illustrated by relevant examples at each stage. The author also discusses more advanced topics, such as molecular dynamics, modeling continuous systems, Monte Carlo methods, genetic algorithm and programming, and numerical renormalization. It includes many more exercises. This can be used as a textbook for either undergraduate or first-year graduate courses on computational physics or scientific computation. It will also be a useful reference for anyone involved in computational research.

Thoroughly revised for its second edition, this advanced textbook provides an introduction to the basic methods of computational physics, and an overview of progress in several areas of scientific computing by relying on free software available from CERN. The book begins by dealing with basic computational tools and routines, covering approximating functions, differential equations, spectral analysis, and matrix operations. Important concepts are illustrated by relevant examples at each stage. The author also discusses more advanced topics, such as molecular dynamics, modeling continuous systems, Monte Carlo methods, genetic algorithm and programming, and numerical renormalization. It includes many more exercises. This can be used as a textbook for either undergraduate or first-year graduate courses on computational physics or scientific computation. It will also be a useful reference for anyone involved in computational research.

This classic textbook, thoroughly revised and updated for its third edition, introduces the basic methods of computational physics. Clear, concise and practical, the new edition includes an additional chapter on machine learning and is supported with sample programs in Python. First, readers are presented with the numerical techniques that every computational scientist should have in their toolbox, including approximation of functions, numerical calculus, differential and partial differential equations, spectral analysis, linear algebra and matrix operations. The author then provides self-contained introductions to the research areas of molecular dynamics, fluid dynamics, Monte Carlo simulations, genetic algorithms and machine learning. Important concepts are illustrated with relevant examples, and each chapter concludes with a selection of exercises. Suitable for upper-division undergraduate to graduate courses on computational physics and scientific computing, this book is also a useful resource for anyone interested in using computation to solve scientific problems.

This classic textbook, thoroughly revised and updated for its third edition, introduces the basic methods of computational physics. Clear, concise and practical, the new edition includes an additional chapter on machine learning and is supported with sample programs in Python. First, readers are presented with the numerical techniques that every computational scientist should have in their toolbox, including approximation of functions, numerical calculus, differential and partial differential equations, spectral analysis, linear algebra and matrix operations. The author then provides self-contained introductions to the research areas of molecular dynamics, fluid dynamics, Monte Carlo simulations, genetic algorithms and machine learning. Important concepts are illustrated with relevant examples, and each chapter concludes with a selection of exercises. Suitable for upper-division undergraduate to graduate courses on computational physics and scientific computing, this book is also a useful resource for anyone interested in using computation to solve scientific problems.