(1) 1-12-26: Course overview; introduction to nonequilibrium thermodynamics:
Video,
Slides.
(2) 1-14-26: Course overview continued, coarse-graining:
Video.
Coarse-graining physical systems (micro- vs. macro-states, seven ways of looking at a protein, turtles all the way down):
Slides.
(3) 1-16-26: Trajectories and ensembles, the basics of probability theory, Bayes' rule:
Video,
Notes.
(4) 1-21-26: Understanding Bayes' rule: posterior, prior, and likelihood; fitting models to data:
Video,
Notes.
(5) 1-23-26: Application of Bayesian model fitting: dense neural networks:
Video,
Notes.
(6) 1-28-26: The Markovian assumption and its consequences: calculating the probabilities of trajectories and states, master equation:
Video,
Notes.
(7) 1-30-26: Simulating trajectories, continuous vs discrete descriptions of system states and time, existence of stationary probabilities:
Video,
Notes.
(8) 2-2-26: Classifying transition graphs (ergodicity and microscopic reversibility), mean hitting times, uniqueness of the stationary distribution for ergodic networks:
Video,
Notes.
(9) 2-4-26: Stationary states and algorithms: Google PageRank; setting up a framework for statistical physics: phase space in classical mechanics:
Video,
Notes.
(10) 2-6-26: Phase space, ergodicity, mixing; Fermi-Pasta-Ulam-Tsingou (FPUT) system: normal modes, canonical transformations:
Video,
Notes.
(11) 2-9-26: FPUT continued, nonlinear perturbations, integrable systems, action-angle coordinates:
Video,
Notes.
(12) 2-11-26: Physics on hypertori (donuts), KAM theorem (deformed donuts), three body problem:
Video,
Notes.
(13) 2-13-26: From integrability to chaos in classical and quantum systems, consequences of ergodicity and mixing, Feynman's ratchet and pawl:
Video,
Notes.
(14) 2-16-26: Macrostates versus microstates, time reversal symmetry:
Video,
Notes.
(15) 2-18-26: Time reversal symmetry continued, deriving the local detailed balance condition:
Video,
Notes.