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E-raamat: Stochastic Thermodynamics: An Introduction

  • Formaat: 272 pages
  • Ilmumisaeg: 06-Jul-2021
  • Kirjastus: Princeton University Press
  • Keel: eng
  • ISBN-13: 9780691215525
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  • Formaat: 272 pages
  • Ilmumisaeg: 06-Jul-2021
  • Kirjastus: Princeton University Press
  • Keel: eng
  • ISBN-13: 9780691215525

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The first comprehensive graduate-level introduction to stochastic thermodynamics

Stochastic thermodynamics is a well-defined subfield of statistical physics that aims to interpret thermodynamic concepts for systems ranging in size from a few to hundreds of nanometers, the behavior of which is inherently random due to thermal fluctuations. This growing field therefore describes the nonequilibrium dynamics of small systems, such as artificial nanodevices and biological molecular machines, which are of increasing scientific and technological relevance.

This textbook provides an up-to-date pedagogical introduction to stochastic thermodynamics, guiding readers from basic concepts in statistical physics, probability theory, and thermodynamics to the most recent developments in the field. Gradually building up to more advanced material, the authors consistently prioritize simplicity and clarity over exhaustiveness and focus on the development of readers’ physical insight over mathematical formalism. This approach allows the reader to grow as the book proceeds, helping interested young scientists to enter the field with less effort and to contribute to its ongoing vibrant development. Chapters provide exercises to complement and reinforce learning.

Appropriate for graduate students in physics and biophysics, as well as researchers, Stochastic Thermodynamics serves as an excellent initiation to this rapidly evolving field.

  • Emphasizes a pedagogical approach to the subject
  • Highlights connections with the thermodynamics of information
  • Pays special attention to molecular biophysics applications
  • Privileges physical intuition over mathematical formalism
  • Solutions manual available on request for instructors adopting the book in a course
Foreword xi
Preface xiii
Acknowledgments xv
Notation xvii
Chapter 1 Motivation
1(5)
1.1 What is stochastic thermodynamics?
1(2)
1.2 Why does it work and why is it useful?
3(1)
1.3 Plan of the work
3(3)
Chapter 2 Basics
6(32)
2.1 Thermodynamics
6(5)
2.2 Thermodynamic efficiency
11(1)
2.3 Free energy and nonequilibrium free energy
12(3)
2.4 Statistical mechanics
15(4)
2.5 Stochastic dynamics
19(2)
2.6 Master equations
21(3)
2.7 Trajectories of master equations
24(2)
2.8 Fokker-Planck equation (*)
26(3)
2.9 Langevin equation (*)
29(3)
2.10 Information
32(3)
2.11 Further reading
35(1)
2.12 Exercises
36(2)
Chapter 3 Stochastic Thermodynamics
38(29)
3.1 The system
38(2)
3.2 Work and heat in stochastic thermodynamics
40(2)
3.3 Mesoscopic and calorimetric heat (*)
42(2)
3.4 ATP hydrolysis by myosin
44(2)
3.5 General reservoirs
46(1)
3.6 Stochastic entropy
47(1)
3.7 Stochastic entropy and entropy production in a manipulated two-level system
48(2)
3.8 Average entropy production rate
50(1)
3.9 Network theory of nonequilibrium steady states (*)
51(2)
3.10 Stochastic chemical reactions
53(2)
3.11 Linear response theory (*)
55(3)
3.12 More on coarse graining (*)
58(4)
3.13 Continuous systems (*)
62(2)
3.14 Further reading
64(1)
3.15 Exercises
65(2)
Chapter 4 Fluctuation Relations
67(37)
4.1 Irreversibility and entropy production
67(2)
4.2 Integral fluctuation relation
69(1)
4.3 Dragged particle on a ring
70(3)
4.4 Back to linear response theory (*)
73(3)
4.5 Detailed fluctuation relation
76(2)
4.6 The Jarzynski and Crooks relations
78(2)
4.7 Instantaneous quench
80(1)
4.8 Fluctuation relations in practice
81(1)
4.9 Adiabatic and nonadiabatic entropy production and the Hatano-Sasa relation
82(2)
4.10 Systems with odd-parity variables
84(2)
4.11 Trajectory probability for Langevin equations (*)
86(1)
4.12 Fluctuation relation for the Langevin equation (*)
87(2)
4.13 Brownian particle in a time-dependent harmonic potential (*)
89(3)
4.14 Brownian motion with inertia (*)
92(2)
4.15 Hamiltonian systems (*)
94(6)
4.16 Further reading
100(1)
4.17 Exercises
101(3)
Chapter 5 Thermodynamics of Information
104(26)
5.1 A brief history
104(2)
5.2 Back to nonequilibrium free energy
106(1)
5.3 Information in stochastic thermodynamics
107(2)
5.4 The Sagawa-Ueda relation
109(1)
5.5 The Mandal-Jarzynski machine
110(2)
5.6 Copying information
112(3)
5.7 Information cost in sensing
115(7)
5.8 Information reservoirs
122(3)
5.9 Fluctuation relations with information reservoirs (*)
125(2)
5.10 Further reading
127(1)
5.11 Exercises
128(2)
Chapter 6 Large Deviations: Theory and Practice
130(30)
6.1 Large deviations in a nutshell
130(4)
6.2 Currents, traffic, and other observables
134(3)
6.3 Large deviations and fluctuation relations
137(1)
6.4 Fluctuation theorem for currents (*)
138(2)
6.5 Tilting
140(2)
6.6 Michaelis-Menten reaction scheme
142(4)
6.7 Fluctuation relations in a model of kinesin (*)
146(5)
6.8 Cloning (*)
151(3)
6.9 Levels of large deviations (*)
154(4)
6.10 Further reading
158(1)
6.11 Exercises
158(2)
Chapter 7 Experimental Applications
160(1)
71 The hairpin as a paradigm
160(12)
7.2 A simpler model
161(1)
7.3 Equilibrium free energies from nonequilibrium manipulations
162(5)
7.4 Maxwell demons
167(1)
7.5 Landauer principle
167(4)
7.6 Further reading
171(1)
Chapter 8 Developments
172(32)
8.1 Stochastic efficiency
172(4)
8.2 Uncertainty relations
176(4)
8.3 Applications of uncertainty relations
180(1)
8.4 First-passage times
181(4)
8.5 Fully irreversible processes
185(2)
8.6 Optimal protocols
187(4)
8.7 Martingales
191(4)
8.8 Random time
195(2)
8.9 Population genetics
197(5)
8.10 Further reading
202(1)
8.11 Exercises
203(1)
Chapter 9 Perspectives
204(3)
Appendixes
207(26)
A.1 Convex functions and the Jensen inequality
207(4)
A.2 Legendre transformation
211(2)
A.3 Probabilities and probability distributions
213(1)
A.4 Generating functions and cumulant generating functions
214(3)
A.5 Ergodic properties of Markov processes
217(2)
A.6 Gillespie algorithm
219(1)
A.7 Derivation of the Fokker-Planck equation
220(2)
A.8 Ito formula and Stratonovich-Ito mapping
222(2)
A.9 Basis of the cycle space
224(1)
A.10 Actions and trajectory probabilities for Langevin equations
225(1)
A.11 The Bennett-Crooks estimator for the free-energy difference
226(2)
A.12 Cauchy-Schwarz inequality
228(1)
A.13 Bound for the current rate function
229(4)
Bibliography 233(8)
Author Index 241(4)
Index 245
Luca Peliti is deputy director of the Santa Marinella Research Institute and professor emeritus of statistical mechanics at the University of Naples Federico II. He is the author of Statistical Mechanics in a Nutshell (Princeton). Simone Pigolotti is associate professor at the Okinawa Institute of Science and Technology, where he leads the Biological Complexity Unit.