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[null,"1.","Record Nr.",null,"UNINA9910460965703321"]
[null,null,"Autore",null,"Landau Rubin H."]
[null,null,"Titolo",null,"Computational physics : problem solving with Python / / Rubin H. Landau, Manuel J. Páez, Cristian C. Bordeianu"]
[null,null,"Pubbl/distr/stampa",null,"Weinheim, Germany : , : Wiley-VCH, , 2015"]
[{"$nulls":4},"©2015"]
[null,null,"ISBN",null,"3-527-68469-7"]
[{"$nulls":4},"3-527-68466-2"]
[null,null,"Edizione",null,"[3rd ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (647 p.)"]
[null,null,"Disciplina",null,"530.02855133"]
[null,null,"Soggetti",null,"Physics - Data processing"]
[{"$nulls":4},"Physics - Computer simulation"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover; Dedication; Copyright page; Contents; Preface; 1 Introduction; 1.1 Computational Physics and Computational Science; 1.2 This Book's Subjects; 1.3 This Book's Problems; 1.4 This Book's Language: The Python Ecosystem; 1.4.1 Python Packages (Libraries); 1.4.2 This Book's Packages; 1.4.3 The Easy Way: Python Distributions (Package Collections); 1.5 Python's Visualization Tools; 1.5.1 Visual (VPython)'s 2D Plots; 1.5.2 VPython's Animations; 1.5.3 Matplotlib's 2D Plots; 1.5.4 Matplotlib's 3D Surface Plots; 1.5.5 Matplotlib's Animations; 1.5.6 Mayavi's Visualizations Beyond Plotting"]
[{"$nulls":4},"1.6 Plotting Exercises 1.7 Python's Algebraic Tools; 2 Computing Software Basics; 2.1 Making Computers Obey; 2.2 Programming Warm up; 2.2.1 Structured and Reproducible Program Design; 2.2.2 Shells, Editors, and Execution; 2.3 Python I/O; 2.4 Computer Number Representations (Theory); 2.4.1 IEEE Floating-Point Numbers; 2.4.2 Python and the IEEE 754 Standard; 2.4.3 Over and Underflow Exercises; 2.4.4 Machine Precision (Model); 2.4.5 Experiment: Your Machine's Precision; 2.5 Problem: Summing Series; 2.5.1 Numerical Summation (Method); 2.5.2 Implementation and Assessment"]
[{"$nulls":4},"3 Errors and Uncertainties in Computations 3.1 Types of Errors (Theory); 3.1.1 Model for Disaster: Subtractive Cancellation; 3.1.2 Subtractive Cancellation Exercises; 3.1.3 Round-off Errors; 3.1.4 Round-off Error Accumulation; 3.2 Error in Bessel Functions (Problem); "]
[{"$nulls":4},"3.2.1 Numerical Recursion (Method); 3.2.2 Implementation and Assessment: Recursion Relations; 3.3 Experimental Error Investigation; 3.3.1 Error Assessment; 4 Monte Carlo: Randomness, Walks, and Decays; 4.1 Deterministic Randomness; 4.2 Random Sequences (Theory); 4.2.1 Random-Number Generation (Algorithm)"]
[{"$nulls":4},"4.2.2 Implementation: Random Sequences 4.2.3 Assessing Randomness and Uniformity; 4.3 Random Walks (Problem); 4.3.1 Random-Walk Simulation; 4.3.2 Implementation: Random Walk; 4.4 Extension: Protein Folding and Self-Avoiding Random Walks; 4.5 Spontaneous Decay (Problem); 4.5.1 Discrete Decay (Model); 4.5.2 Continuous Decay (Model); 4.5.3 Decay Simulation with Geiger Counter Sound; 4.6 Decay Implementation and Visualization; 5 Differentiation and Integration; 5.1 Differentiation; 5.2 Forward Difference (Algorithm); 5.3 Central Difference (Algorithm); 5.4 Extrapolated Difference (Algorithm)"]
[{"$nulls":4},"5.5 Error Assessment 5.6 Second Derivatives (Problem); 5.6.1 Second-Derivative Assessment; 5.7 Integration; 5.8 Quadrature as Box Counting (Math); 5.9 Algorithm: Trapezoid Rule; 5.10 Algorithm: Simpson's Rule; 5.11 Integration Error (Assessment); 5.12 Algorithm: Gaussian Quadrature; 5.12.1 Mapping Integration Points; 5.12.2 Gaussian Points Derivation; 5.12.3 Integration Error Assessment; 5.13 Higher Order Rules (Algorithm); 5.14 Monte Carlo Integration by Stone Throwing (Problem); 5.14.1 Stone Throwing Implementation; 5.15 Mean Value Integration (Theory and Math); 5.16 Integration Exercises"]
[{"$nulls":4},"5.17 Multidimensional Monte Carlo Integration (Problem)"]
[null,null,null,"Sommario/riassunto","The use of computation and simulation has become an essential part of the scientific process. Being able to transform a theory into an algorithm requires significant theoretical insight, detailed physical and mathematical understanding, and a working level of competency in programming.  This upper-division text provides an unusually broad survey of the topics of modern computational physics from a multidisciplinary, computational science point of view. Its philosophy is rooted in learning by doing (assisted by many model programs), with new scientific materials as well as with the Python program"]