Manuel José Páez – författare

Our future scientists and professionals must be conversant in computational techniques. In order to facilitate integration of computer methods into existing physics courses, Computational Problems for Physics offers a large number of worked examples and problems with fully guided solutions in Python (as well as Mathematica, Java, C, Fortran, and Maple on the Web). The book can be used as a self-study guide for learning how to use computer methods in physics. Fully revised, this second edition includes:A chapter on neural networks, machine learning, and artificial intelligence, with the building of simple networks, and the use of machine learning software.A chapter on quantum computing with some problems to be run on the IBM Quantum Computer.A chapter on general relativity with manipulations of the field equations and with computation of GR corrections to classical mechanics.An expanded coverage of principal component analysis.A crucial resource for students beginning their study of computational physics with in-depth and engaging problems and exercises.

Our future scientists and professionals must be conversant in computational techniques. In order to facilitate integration of computer methods into existing physics courses, Computational Problems for Physics offers a large number of worked examples and problems with fully guided solutions in Python (as well as Mathematica, Java, C, Fortran, and Maple on the Web). The book can be used as a self-study guide for learning how to use computer methods in physics. Fully revised, this second edition includes:A chapter on neural networks, machine learning, and artificial intelligence, with the building of simple networks, and the use of machine learning software.A chapter on quantum computing with some problems to be run on the IBM Quantum Computer.A chapter on general relativity with manipulations of the field equations and with computation of GR corrections to classical mechanics.An expanded coverage of principal component analysis.A crucial resource for students beginning their study of computational physics with in-depth and engaging problems and exercises.

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:• Detailed explanations and solutions in various coding languages.• Problems are ranked based on computational and physics difficulty.• Basics of numerical methods covered in an introductory chapter.• Programming guidance via flowcharts and pseudocode.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).Manuel Jose Paez-Mejia is a Professor of Physics at Universidad de Antioquia in Medellín, Colombia.