Computational Physics I: Numerical Methods - Rilegato

Scherer, Philipp O. J.

Rilegato

ISBN 10: 3032078555 ISBN 13: 9783032078551

Casa editrice: Springer Nature, 2026

Riassunto

This book presents basic numerical methods and applies them to a large variety of physical models in multiple computer experiments. Authored by a distinguished expert in the field, it combines rigorous theoretical insights with a wealth of practical and easily accessible computational applications. This book serves as an ideal standalone text for computational physics courses at both the graduate and advanced undergraduate levels. It offers a detailed and cohesive exploration of the physics of classical and quantum systems, electrostatics, thermodynamics, statistical physics and nonlinear systems, integrating foundational principles with advanced simulation techniques.

The significantly expanded and updated fourth edition comprises two volumes. Volume 1 is dedicated to numerical methods, covering essential topics such as error analysis, numerical differentiation and integration, Fourier transforms, time-frequency analysis, and data fitting. Alongside this, it presents essential computational methods such as Monte Carlo techniques and solving Newton's equations of motion, equipping readers with the tools necessary for practical problem-solving in computational physics. New in this book is an introduction to artificial neural networks (ANNs) for elementary tasks such as classification, regression, interpolation, time series analysis and principal component analysis. It features methods for solving differential equations with ANNs, including a discussion on the concept of “automatic differentiation” as a necessary alternative to analytical, numerical, and symbolic differentiation. These additions offer readers deeper insights and more robust tools for their studies and research.

Su questo libro

Informazioni sull'autore

Prof. Scherer received his Ph.D. in experimental and theoretical physics in 1984. He habilitated in theoretical physics and has been a lecturer at the Technical University of Munich (TUM) since 1999. He joined the National Institute of Advanced Industrial Science and Technology (AIST) in Tsukuba, Japan, as a visiting scientist in 2001 and 2003. From 2006 to 2008, he has been temporary leader of the Institute for Theoretical Biomolecular Physics at TUM. Ever since, he has been an adjunct professor at the physics faculty of TUM. His area of research includes biomolecular physics and the computer simulation of molecular systems with classical and quantum methods. He published books on theoretical molecular physics and computational physics. 

Dalla quarta di copertina

This book presents basic numerical methods and applies them to a large variety of physical models in multiple computer experiments. Authored by a distinguished expert in the field, it combines rigorous theoretical insights with a wealth of practical and easily accessible computational applications. This book serves as an ideal standalone text for computational physics courses at both the graduate and advanced undergraduate levels. It offers a detailed and cohesive exploration of the physics of classical and quantum systems, electrostatics, thermodynamics, statistical physics and nonlinear systems, integrating foundational principles with advanced simulation techniques.

The significantly expanded and updated fourth edition comprises two volumes. Volume 1 is dedicated to numerical methods, covering essential topics such as error analysis, numerical differentiation and integration, Fourier transforms, time-frequency analysis, and data fitting. Alongside this, it presents essential computational methods such as Monte Carlo techniques and solving Newton's equations of motion, equipping readers with the tools necessary for practical problem-solving in computational physics. New in this book is an introduction to artificial neural networks (ANNs) for elementary tasks such as classification, regression, interpolation, time series analysis and principal component analysis. It features methods for solving differential equations with ANNs, including a discussion on the concept of “automatic differentiation” as a necessary alternative to analytical, numerical, and symbolic differentiation. These additions offer readers deeper insights and more robust tools for their studies and research.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 10: 3032078555
• ISBN 13: 9783032078551
• Rilegatura: Copertina rigida
• Numero edizione: 4
• Numero di pagine: 500
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3,69115 Heidelberg, Germany; Heidelberg; 69115; Germania