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Computational Physics, 4 ed
出版済み 3-5週間でお届けいたします。
Problem Solving with Python Author: Landau, Rubin H. (Oregon State University, Corvallis) / Paez, Manuel J. (University of Antioquia, Medellin, Colombia) / Bordeianu, Cristian C. (Bucharest University, Romania) Publisher: VCH ISBN: 9783527414253 Cover: PAPERBACK Date: 2024年04月 DESCRIPTION The classic in the field for more than 25 years, now with more emphasis on data science and machine learning Computational physics combines physics, applied mathematics, and computer science in a cutting-edge multidisciplinary approach to solving realistic physical problems. It has become integral to modern physics research because of its capacity to bridge the gap between mathematical theory and real-world system behavior. Computational Physics provides the reader with the essential knowledge to understand computational tools and mathematical methods well enough to be successful. Its philosophy is rooted in “learning by doing”, assisted by many sample programs in the popular Python programming language. The first third of the book lays the fundamentals of scientific computing, including programming basics, stable algorithms for differentiation and integration, and matrix computing. The latter two-thirds of the textbook cover more advanced topics such linear and nonlinear differential equations, chaos and fractals, Fourier analysis, nonlinear dynamics, and finite difference and finite elements methods. A particular focus in on the applications of these methods for solving realistic physical problems. Readers of the fourth edition of Computational Physics will also find: Brand-new chapters on general relativity and the computational physics of soft matter An exceptionally broad range of topics, from simple matrix manipulations to intricate computations in nonlinear dynamics A whole suite of supplementary material: Python programs, Jupyter notebooks and videos Computational Physics is ideal for students in physics, engineering, materials science, and any subjects drawing on applied physics. TABLE OF CONTENTS 1. Introduction 2. Python Programming Basics and Visualizations 3. Errors and Uncertainties in Computations 4. Monte Carlo: Randomness, Walks, and Decays 5. Differentiation 6. Integration 7. Matrix Computing 8. Trial-and-Error Searching and Data Fitting 9. Differential Equations & Nonlinear Oscillations 10. ODE Applications: Eigenvalues, Scattering, Projectiles 11. Program Optimization, Tuning, and GPU Programming 12. Data Science I: Fourier Analysis 13. Data Science II: Wavelet and Principal Components Analyses for Nonstationary Signals & Data Compression 14. Neural Nets and Machine Learning 15. Nonlinear Population Dynamics 16. Continuous Nonlinear Dynamics 17. Fractals and Statistical Growth Models 18. Thermodynamic Simulations 21. Molecular Dynamics Simulations 22. Soft Matter and Jamming 23. Electrostatics via Finite Differences 24. Electrostatics via Finite Elements 25. Heat Flow via Time Stepping 26. Waves on Strings and Membranes 27. Quantum Wave Packets 28. Electromagnetic Waves 29. Shocks Waves and Solitons 30. General Relativity 31. Appendices 最近チェックした商品
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