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 13
... stochastic or a random process . Thus , a stochastic process involves a stochastic variable that takes random values whose development in time is not governed by a deterministic equation . Only the probabilities that the random variable ...
... stochastic or a random process . Thus , a stochastic process involves a stochastic variable that takes random values whose development in time is not governed by a deterministic equation . Only the probabilities that the random variable ...
Page 213
... stochastic process . Accordingly , we now study the mathematics of Markovian processes . 9.3 MATHEMATICAL PRELIMINARIES 9.3.1 Markovian Processes As mentioned in Section 1.3 , a stochastic process is the development of a random variable ...
... stochastic process . Accordingly , we now study the mathematics of Markovian processes . 9.3 MATHEMATICAL PRELIMINARIES 9.3.1 Markovian Processes As mentioned in Section 1.3 , a stochastic process is the development of a random variable ...
Page 226
... stochastic variable is called a time series in the field of statistical analysis . Since Brownian motion consists of a series of values of the position coordinate at different times , it is possible to treat it by time series analysis ...
... stochastic variable is called a time series in the field of statistical analysis . Since Brownian motion consists of a series of values of the position coordinate at different times , it is possible to treat it by time series analysis ...
Contents
Classical Statistical Mechanics | 3 |
Quantum Statistical Mechanics | 45 |
Ideal BoseEinstein and FermiDirac gases | 67 |
Copyright | |
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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 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 p₁ partition function Pathria phase space phase transition Phys physical polymer potential probability density protein quantum mechanics RANDOM NUMBER random walk scaling Section shown in Figure simulation solution spin glass statistical mechanics steps stochastic temperature theory thermodynamic limit total number trajectory values vector velocity Wolfram zero Zwanzig