Statistical mechanics is our tool for deriving the laws that emerge from complex systems. Sethna's text distills the subject to be accessible to those in all realms of science and engineering -- avoiding extensive use of quantum mechanics, thermodynamics, and molecular physics.
Statistical mechanics explains how bacteria search for food, and how DNA replication is proof-read in biology; optimizes data compression, and explains transitions in complexity in computer science; explains the onset of chaos, and launched random matrix theory in mathematics; addresses extreme events in engineering; and models pandemics and language usage in the social sciences. Sethna's exercises introduce physicists to these triumphs and a hundred others -- broadening the horizons of scholars both practicing and nascent.
Flipped classrooms and remote learning can now rely on 33 pre-class exercises that test reading comprehension (Emergent vs. fundamental; Weirdness in high dimensions; Aging, entropy and DNA), and 70 in-class activities that illuminate and broaden knowledge (Card shuffling; Human correlations; Crackling noises). Science is awash in information, providing ready access to definitions, explanations, and pedagogy. Sethna's text focuses on the tools we use to create new laws, and on the fascinating simple behavior in complex systems that statistical mechanics explains.
By:
James P. Sethna (Professor of Physics Cornell University)
Imprint: Oxford University Press
Country of Publication: United Kingdom
Edition: 2nd Revised edition
Volume: 14
Dimensions:
Height: 23mm,
Width: 189mm,
Spine: 246mm
Weight: 1.012kg
ISBN: 9780198865254
ISBN 10: 0198865252
Series: Oxford Master Series in Physics
Pages: 496
Publication Date: 29 January 2021
Audience:
College/higher education
,
Primary
Format: Paperback
Publisher's Status: Active
Preface Contents List of figures What is statistical mechanics? 1.1: Quantum dice and coins 1.2: Probability distributions 1.3: Waiting time paradox 1.4: Stirling>'s formula 1.5: Stirling and asymptotic series 1.6: Random matrix theory 1.7: Six degrees of separation 1.8: Satisfactory map colorings 1.9: First to fail: Weibull 1.10: Emergence 1.11: Emergent vs. fundamental 1.12: Self-propelled particles 1.13: The birthday problem 1.14: Width of the height distribution 1.15: Fisher information and Cram´erDSRao 1.16: Distances in probability space Random walks and emergent properties 2.1: Random walk examples: universality and scale invariance 2.2: The diffusion equation 2.3: Currents and external forces 2.4: Solving the diffusion equation Temperature and equilibrium 3.1: The microcanonical ensemble 3.2: The microcanonical ideal gas 3.3: What is temperature? 3.4: Pressure and chemical potential 3.5: Entropy, the ideal gas, and phase-space refinements Phase-space dynamics and ergodicity 4.1: Liouville>'s theorem 4.2: Ergodicity Entropy 5.1: Entropy as irreversibility: engines and the heat death of the Universe 5.2: Entropy as disorder 5.3: Entropy as ignorance: information and memory Free energies 6.1: The canonical ensemble 6.2: Uncoupled systems and canonical ensembles 6.3: Grand canonical ensemble 6.4: What is thermodynamics? 6.5: Mechanics: friction and fluctuations 6.6: Chemical equilibrium and reaction rates 6.7: Free energy density for the ideal gas Quantum statistical mechanics 7.1: Mixed states and density matrices 7.2: Quantum harmonic oscillator 7.3: Bose and Fermi statistics 7.4: Non-interacting bosons and fermions 7.5: MaxwellDSBoltzmann 's regression hypothesis and time correlations 10.5: Susceptibility and linear response 10.6: Dissipation and the imaginary part 10.7: Static susceptibility 10.8: The fluctuation-dissipation theorem 10.9: Causality and KramersDSKr¨onig Abrupt phase transitions 11.1: Stable and metastable phases 11.2: Maxwell construction 11.3: Nucleation: critical droplet theory 11.4: Morphology of abrupt transitions Continuous phase transitions 12.1: Universality 12.2: Scale invariance 12.3: Examples of critical points A Appendix: Fourier methods A.1: Fourier conventions A.2: Derivatives, convolutions, and correlations A.3: Fourier methods and function space A.4: Fourier and translational symmetry References Index
James P. Sethna is professor of physics at Cornell University. Sethna has used statistical mechanics to make substantive contributions in a bewildering variety of subjects -- mathematics (dynamical systems and the onset of chaos), engineering (microstructure, plasticity, and fracture), statistics (information geometry, sloppy models, low-dimensional embeddings), materials science (glasses and spin glasses, liquid crystals, crackling noise, superconductivity), and popular culture (mosh pit dynamics and zombie outbreak epidemiology). He has collected cool, illustrative problems from students and colleagues over the decades, which inspired this textbook.
Reviews for Statistical Mechanics: Entropy, Order Parameters, and Complexity
Sethna's book provides an important service to students who want to learn modern statistical mechanics. The text teaches students how to work out problems by guiding them through the exercises rather than by presenting them with worked-out examples. * Susan Coppersmith, Physics Today, May 2007 * Review from previous edition Since the book treats intersections of mathematics, biology, engineering, computer science and social sciences, it will be of great help to researchers in these fields in making statistical mechanics useful and comprehensible. At the same time, the book will enrich the subject for physicists who'd like to apply their skills in other disciplines. [...] The author's style, although quite concentrated, is simple to understand, and has many lovely visual examples to accompany formal ideas and concepts, which makes the exposition live and intuitvely appealing. * Olga K. Dudko, Journal of Statistical Physics, Vol 126 *
