Thermodynamic speed limits from the regression of information

TL;DR

This paper demonstrates how thermodynamic speed limits and uncertainty relations for nonlinear irreversible processes can be derived from linear regression models, linking information theory and thermodynamics.

Contribution

It introduces a novel framework connecting linear regression, information theory, and thermodynamic speed limits for nonequilibrium processes.

Findings

Uncertainty relations mirror quantum mechanics uncertainty relations.

Optimal linear models predict equations of motion using information-theoretic variables.

Second law constrains the slope of thermodynamic observables, giving a geometric interpretation.

Abstract

Irreversible processes accomplished in a fixed time involve nonlinearly coupled flows of matter, energy, and information. Here, using entropy production as an example, we show how thermodynamic uncertainty relations and speed limits on these nonlinear processes derive from linear regression. These uncertainty relations hold for both passive and actively-driven nonequilibrium processes and all have a mathematical form that mirrors uncertainty relations in quantum mechanics. Using optimal linear models, we show that information-theoretic variables naturally give physical predictions of the equation of motion on statistical manifolds in terms of physical observables. In these models, optimal intercepts are related to nonequilibrium analogs of Massieu functions/thermodynamic potentials, and optimal slopes are related to speed limits on collections of thermodynamic observables. Within this formalism, the second law of thermodynamics has a geometric interpretation as the nonnegativity of the slope and constrains the equation of motion. Overall, our results suggest that unknown relationships between nonequilibrium variables can be learned through statistical-mechanical inference.