Statistical Field Theory

(Spring 2017)



  1. •Lectures: Class P103; Sat. & Mon., 9:30-11:00

  2. •Tutorials: Class P103; Wed. 11:00-12:30



Lecturer: Ali G. Moghaddam

   

Teaching Assistant: Hadi Khanjani


Course's syllabus:


  1. •Introduction to field theory 

  2. •Collective modes (symmetries & dimensionality; phase transitions and critical phenomena)

  3. •The Landau-Ginzburg theory (Mean-field; critical exponents)

  4. •Fluctuations and Goldstone modes (upper and the lower critical dimensions)

  5. •Universality & renormalization group (RG) (Self-similarity; the scaling hypothesis; Kadanoff's heuristic RG),

  6. •Perturbation Theory (Diagrammatic expansions; Wilson's momentum space RG)

  7. •Lattice Models (Ising, potts, etc.; position-space RGs:Cumulant, Migdal-Kadanoff)

  8. •Dynamics of statistical fields: Langevin, Fokker-Planck equations; conservation laws)

  9. •Random system & fields (a bird’s eye view)


References:


  1. •Statistical Physics of Fields: Mehran  Kardar (Cambridge University Press, 2007) [main textbook]

  2. •Lectures On Phase Transitions And The Renormalization Group , Nigel Goldenfeld (Addison-Wesley, 1992)

  3. •Statistical Field Theory, Giorgio Parisi, (Addison-Wesley, 1988)


Homework Assignments:


         Any problem set should be retuned back with your solutions after two weeks.


Exams:

  1. •Midterm exam: 21 Ordibehesht 1396

  2. •Final exam: 2 Tir 1396


Course's Evaluation: 

Final grades will be based on: homework assignments (30%) + midterm exam (30%) + final exam (40%)



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Statistical Mechanics II

(Winter 2017)



  1. •Lectures: Class P104; Sun. & Thur., 9:30-11:00

  2. •Tutorials: Class P104; Mon. 15:30-17:00



Lecturer: Ali G. Moghaddam

   

Teaching Assistant: Hadi Khanjani


Course's syllabus:


  1. •Introduction to thermodynamics (thermal equilibrium, the laws of thermodynamics; temperature, energy, entropy)

  2. •Probability Theory (random variables, probability cumulants and correlations; central limit theorem, law of large numbers)

  3. •Classical Statistical Mechanics (postulates, micro-macro relations: Boltzmann entropy formula, microcanonical ensemble)

  4. •Canonical and grand canonical ensembles (Boltzmann weight function, Gibbs theory, non-interacting examples)


References:


  1. •Statistical Physics of Particles: Mehran  Kardar (Cambridge University Press, 2007) [main textbook]

  2. •Statistical Mechanics, Kerson Huang (2nd ed. Wiley, 1987)

  3. •Statistical Mechanics, R. K. Pathria & Paul D. Beale  (3rd edition, Academic Press, 2011)


Homework Assignments:


         Any problem set should be retuned back with your solutions after two weeks.


Exams:

  1. •Midterm exam: 14 Bahman 1395

  2. •Final exam: 22 Esfand 1395


Course's Evaluation: 

Final grades will be based on: homework assignments (30%) + midterm exam (30%) + final exam (40%)



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Statistical Mechanics I

(Fall 2016)



  1. •Lectures: Class P103; Sun. & Thur., 11:00-12:30

  2. •Tutorials: Class P103; Wed. 9:30-11:00



Lecturer: Ali G. Moghaddam

   

Teaching Assistant: Hadi Khanjani


Course's syllabus:


  1. •Kinetic Theory (phase-space; Liouville's theorem, BBGKY hierarchy)

  2. •Boltzmann equation (transport phenomena, hydrodynamic variables & conservation laws)

  3. •Interacting Systems (virial and cluster expansions, van der Waals theory, liquid-vapor condensation)

  4. •Quantum Statistical Mechanics (quantization effects in molecular gases, phonons, photons)

  5. •Density matrix formulation

  6. •Identical Particles

  7. •Degenerate quantum gases I: Fermi (Fermi-Dirac statistics, Somerfeld expansion, Landau diamagnetism, etc.)

  8. •Degenerate quantum gases I: Bose (Bose-Einstein statistics, condensation, superfluidity)


References:


  1. •Statistical Physics of Particles: Mehran  Kardar (Cambridge University Press, 2007) [main textbook]

  2. •Statistical Mechanics, Kerson Huang (2nd ed. Wiley, 1987)

  3. •Statistical Mechanics, R. K. Pathria & Paul D. Beale  (3rd edition, Academic Press, 2011)


Homework Assignments:


         Any problem set should be retuned back with your solutions after two weeks.


Exams:

  1. •Midterm exam: 12 Aban 1395

  2. •Final exam: 16 Azar 1395


Course's Evaluation: 

Final grades will be based on: homework assignments (30%) + midterm exam (30%) + final exam (40%)



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