Quantifying dissipation using fluctuating currents.

Area of Science:

• Thermodynamics
• Statistical Mechanics
• Nonlinear Dynamics

Background:

• Systems coupled to multiple thermodynamic reservoirs can exhibit nonequilibrium dynamics, generating currents and increasing reservoir entropy.
• The rate of entropy production quantifies statistical irreversibility and indicates whether a system is in or out of equilibrium.
• Recent experiments have used irreversibility measurements to detect biological systems operating far from equilibrium.

Purpose of the Study:

• To propose three strategies for quantifying entropy production rates, moving beyond binary equilibrium/nonequilibrium classification.
• To illustrate these strategies using an analytically tractable bead-spring model.
• To address the challenges of data requirements in high-dimensional systems.

Main Methods:

• Generating time-series data for a bead-spring model.
• Inferring probability currents to indirectly quantify entropy production.
• Utilizing the thermodynamic uncertainty relation to bound entropy production rates.

Main Results:

• Direct quantification of entropy production offers a more nuanced understanding of nonequilibrium processes.
• Inferring probability currents is a viable method for entropy production estimation.
• The thermodynamic uncertainty relation can mitigate the 'curse of dimensionality' in high-dimensional systems.

Conclusions:

• Quantifying entropy production rates provides a more informative measure of irreversibility than binary classification.
• The thermodynamic uncertainty relation offers a practical approach to estimate entropy production in complex systems.
• This work advances the understanding and analysis of nonequilibrium dynamics in physical and biological systems.