Description
Taylor & Francis Computational Problems For Physics With Guided Solutions Using Python by Rubin H. Landau
Our future scientists and professionals must be conversant in computational techniques. In order to facilitate integration of computer methods into existing physics courses, this textbook offers a large number of worked examples and problems with fully guided solutions in Python as well as other languages (Mathematica, Java, C, Fortran, and Maple). It's also intended as a self-study guide for learning how to use computer methods in physics. The authors include an introductory chapter on numerical tools and indication of computational and physics difficulty level for each problem. Readers also benefit from the following features:_x000D__x000D__x000D_* Detailed explanations and solutions in various coding languages._x000D__x000D__x000D_* Problems are ranked based on computational and physics difficulty._x000D__x000D__x000D_* Basics of numerical methods covered in an introductory chapter._x000D__x000D__x000D_* Programming guidance via flowcharts and pseudocode._x000D__x000D__x000D_Rubin Landau is a Distinguished Professor Emeritus in the Department of Physics at Oregon State University in Corvallis and a Fellow of the American Physical Society (Division of Computational Physics)._x000D__x000D__x000D_Manuel Jose Paez-Mejia is a Professor of Physics at Universidad de Antioquia in Medellin, Colombia._x000D_ _x000D_
1 Computational Basics for Physics_x000D_
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2 Data Analytics for Physics_x000D_
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3 Classical & Nonlinear Dynamics_x000D_
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4 Wave Equations & Fluid Dynamics_x000D_
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5 Electricity & Magnetism_x000D_
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6 Quantum Mechanics_x000D_
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7 Thermodynamics & Statistical Physics_x000D_
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8 Biological Models: Population Dynamics & Plant Growth_x000D_
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9 Additional Entry-Level Problems_x000D_
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Appendix: Python Codes_x000D_