# Teaching the Principles of Statistical Dynamics

Source: [Teaching the Principles of Statistical Dynamics](<https://www.sota2.com/research/paper/68fa31b81e209131ce8810df>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-24T19:01:20.400Z

Authors: Kingshuk Ghosh, Ken Dill, Mandar M. Inamdar, Effrosyni Seitaridou, Rob Phillips

Published: 2005-07-16T01:56:56.000Z

## Abstract

We describe a simple framework for teaching the principles that underlie the dynamical laws of transport: Fick's law of diffusion, Fourier's law of heat flow, the Newtonian viscosity law, and mass-action laws of chemical kinetics. In analogy with the way that the maximization of entropy over microstates leads to the Boltzmann law and predictions about equilibria, maximizing a quantity that E. T. Jaynes called "Caliber" over all the possible microtrajectories leads to these dynamical laws. The principle of Maximum Caliber also leads to dynamical distribution functions which characterize the relative probabilities of different microtrajectories. A great source of recent interest in statistical dynamics has resulted from a new generation of single-particle and single-molecule experiments which make it possible to observe dynamics one trajectory at a time.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/cond-mat/0507388>)

[PDF](<https://arxiv.org/pdf/cond-mat/0507388.pdf>)
