Statistical Mechanics: Fundamentals and Modern ApplicationsA valuable learning tool for students and an indispensable resource for professional scientists and engineers Several outstanding features make this book a superior introduction to modern statistical mechanics: It is the only intermediate-level text offering comprehensive coverage of both basic statistical mechanics and modern topics such as molecular dynamic methods, renormalization theory, chaos, polymer chain folding, oscillating chemical reactions, and cellular automata. It is also the only text written at this level to address both equilibrium and nonequilibrium statistical mechanics. Finally, students and professionals alike will appreciate such aids to comprehension as detailed derivations for most equations, more than 100 chapter-end exercises, and 15 computer programs written in FORTRAN that illustrate many of the concepts covered in the text. Statistical Mechanics begins with a refresher course in the essentials of modern statistical mechanics which, on its own, can serve as a handy pocket guide to basic definitions and formulas. Part II is devoted to equilibrium statistical mechanics. Readers will find in-depth coverage of phase transitions, critical phenomena, liquids, molecular dynamics, Monte Carlo techniques, polymers, and more. Part III focuses on nonequilibrium statistical mechanics and progresses in a logical manner from near-equilibrium systems, for which linear responses can be used, to far-from-equilibrium systems requiring nonlinear differential equations. |
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Page 302
Fundamentals and Modern Applications Richard E. Wilde, Surjit Singh. Log of Power Spectrum 2 3 -1 0 6 0.0 20 2.0 4.0 6.0 8.0 10.0 Frequency Figure 12.8 A portion of the power spectrum of the time series in Figure 12.5 ( a ) . of the power ...
Fundamentals and Modern Applications Richard E. Wilde, Surjit Singh. Log of Power Spectrum 2 3 -1 0 6 0.0 20 2.0 4.0 6.0 8.0 10.0 Frequency Figure 12.8 A portion of the power spectrum of the time series in Figure 12.5 ( a ) . of the power ...
Page 311
... power spectrum . The time series of the logistic equation for a 3.5 , 3.84 , and 3.9 are shown in Figure 12.16 . The lines between the square dots are a guide to the eye . These time series were calculated using the FORTRAN program ...
... power spectrum . The time series of the logistic equation for a 3.5 , 3.84 , and 3.9 are shown in Figure 12.16 . The lines between the square dots are a guide to the eye . These time series were calculated using the FORTRAN program ...
Page 376
... POWER SPECTRUM THE INPUT FOR THE POWER SPECTRUM CONSISTS OF A TIME SERIES , EITHER CALCULATED OR MEASURED IN AN EXPERIMENT . THESE DATA ARE IN A FILE NAMED ' DATA . ' THE NUMBER OF DATA POINTS OF THE TIME SERIES MUST BE PROVIDED ...
... POWER SPECTRUM THE INPUT FOR THE POWER SPECTRUM CONSISTS OF A TIME SERIES , EITHER CALCULATED OR MEASURED IN AN EXPERIMENT . THESE DATA ARE IN A FILE NAMED ' DATA . ' THE NUMBER OF DATA POINTS OF THE TIME SERIES MUST BE PROVIDED ...
Contents
Classical Statistical Mechanics | 3 |
Quantum Statistical Mechanics | 45 |
Phase Transitions and Critical Phenomena | 83 |
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Common terms and phrases
A₁ attractor automata average behavior bifurcation Boltzmann Boltzmann equation Brownian motion Brownian particle BZ reaction calculate called cell cellular automaton chaos Chapter Chem chemical reactions coefficient coordinates correlation functions critical point defined derivation discussed distribution function dynamics eigenvalues equation equilibrium evaluated exponents ferromagnetic fixed point fluctuations fluid FORMAT(1X FORTRAN FORTRAN program fractal dimension free energy Gaussian given Hamiltonian initial configuration integral interactions Ising model ITERATE Kramers Langevin equation lattice limit cycle linear logistic magnetization Markovian matrix mean-field method microstates molecular molecules Monte Carlo NEIGHBORHOOD nonequilibrium nonlinear number of particles obtained one-dimensional oscillating partition function Pathria phase space phase transition Phys physical polymer potential probability density protein quantum mechanics r₁ RANDOM NUMBER random walk scaling Section shown in Figure simulation solution spin glass statistical mechanics steps stochastic temperature theory thermodynamic limit total number values vector velocity Wolfram zero Zwanzig