LEADER 05326nam 2200625 450 001 9910460965703321 005 20200520144314.0 010 $a3-527-68469-7 010 $a3-527-68466-2 035 $a(OCoLC)918556558 035 $a(JP-MeL)3000065401 035 $a(MiAaPQ)EBC1977650 035 $a(MiAaPQ)EBC4042562 035 $a(Au-PeEL)EBL4042562 035 $a(CaPaEBR)ebr11115100 035 $a(CaONFJC)MIL802201 035 $a(OCoLC)927509579 035 $a(EXLCZ)993710000000434409 100 $a20151106h20152015 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aComputational physics $eproblem solving with Python /$fRubin H. Landau, Manuel J. Pa?ez, Cristian C. Bordeianu 205 $a3rd ed. 210 1$aWeinheim, Germany :$cWiley-VCH,$d2015. 210 4$dİ2015 215 $a1 online resource (647 p.) 300 $aDescription based upon print version of record. 311 $a3-527-41315-4 320 $aIncludes bibliographical references and index. 327 $aCover; 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 327 $a1.6 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 327 $a3 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); 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) 327 $a4.2.2 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) 327 $a5.5 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 327 $a5.17 Multidimensional Monte Carlo Integration (Problem) 330 $aThe 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 606 $aPhysics$vProblems, exercises, etc$xData processing 606 $aPhysics$xComputer simulation 608 $aElectronic books. 615 0$aPhysics$xData processing. 615 0$aPhysics$xComputer simulation. 676 $a530.02855133 700 $aLandau$b Rubin H.$060004 702 $aPa?ez$b Manuel J. 702 $aBordeianu$b Cristian C. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910460965703321 996 $aComputational physics$9263563 997 $aUNINA