- Publisher's listprice EUR 106.99
-
41 790 Ft (39 800 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 8 358 Ft off)
- Discounted price 33 432 Ft (31 840 Ft + 5% VAT)
- Discount is valid until: 30 June 2026
Subcribe now and take benefit of a favourable price.
Subscribe
36 775 Ft
Availability
Not yet published.
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Edition number 4
- Publisher Springer Nature Switzerland
- Date of Publication 9 July 2026
- ISBN 9783032078551
- Binding Hardback
- No. of pages423 pages
- Size 235x155 mm
- Language English
- Illustrations XIX, 423 p. 157 illus., 49 illus. in color. 700
Categories
Long description:
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.
MoreTable of Contents:
1.Error Analysis.-2.Interpolation.- 3.Differentiation.- 4.Integration.- 5.Systems of Inhomogeneous Linear Equations.- 6.Roots and Extremal Points.- 7.Fourier Transformation.- 8.Time-Frequency Analysis.- 9.Random Numbers and Monte Carlo Methods.- 10.Eigenvalue Problems.- 11.Data Fitting.- 12.Data Analysis with Arti cial Neural Networks.- 13.Discretization of Di erential Equations.- 14.Equations of Motion.
More
