Lectures: Advanced Statistical Physics
Advanced Statistical Physics
How do macroscopic laws and emergent structures arise from the random motion of microscopic particles? By using probability theory, symmetry, and coarse-graining, we can build robust predictive theories. This course explains the statistical rules governing complex systems, bridging the gap between thermal equilibrium and the unexplored frontiers of non-equilibrium statistical physics.
Scope
Complex systems spanning soft matter, topological phases, biological networks, and artificial intelligence. Emphasis is placed on emergent properties, spontaneous symmetry breaking, scale invariance, and the consequences of breaking detailed balance. Methods include continuum field theories, renormalization group (RG) analysis, stochastic dynamics, and information theory.
Themes
- Stochastic thermodynamics & fluctuation theory
- Ginzburg-Landau theory & phase transitions
- Critical phenomena & renormalization group theory
- Nonequilibrium physics & Onsager theory
- Active matter & far-from-equilibrium theory
- Statistical mechanics of machine learning
Lectures will be updated on YouTube. The first lecture will take place on April 13th.