Learning, Information Theory, and Nonequilibrium Statistical Mechanics
Learning, Information Theory, and Nonequilibrium Statistical Mechanics
Berkeley workgroup on
Learning, Information Theory, & Nonequilibrium
Thermodynamics
Coordinates
EMAIL lineq (at) lists.berkeley.edu
LOCATION 560 Evans Halls, UC Berkeley
TIme 3:30 PM every other Friday (kinda)
WEB https://calmail.berkeley.edu/manage/list/listinfo/lineq@lists.berkeley.edu
Grant Rotskoff (UCB): Levels without loops: An introduction to large deviations in nonequilibrium statistical physics
29 April 2016
The mathematical tools provided by large deviation theory enable a probabilistic description of spacetime observables, like generalized currents. Because currents (e.g., entropy production) are one way to characterize physical systems driven out of equilibrium, large deviation theory has emerged as the de facto language of nonequilibrium statistical physics. In this talk, I will review some of the basic concepts and applications of the theory to Markov jump processes and diffusion processes. I will introduce the level 1, 2, and 2.5 rate functions and discuss some recent results relevant to nonequilibrium dynamics.
Reference:
[1] H. Touchette, Physics Reports 478, 1 (2009). [doi: http://dx.doi.org/10.1016/j.physrep.2009.05.002]
[2] A. C. Barato and R. Chetrite, Journal of Statistical Physics 160, 1154 (2015). [doi: http://dx.doi.org/10.1007/s10955-015-1283-0]