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This study introduces new model-free tools to detect and quantify nonequilibrium thermodynamics in complex systems. These methods analyze multifilar events, offering insights into entropy production rates even with limited data.

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Area of Science:

  • Thermodynamics
  • Statistical Mechanics
  • Complex Systems

Background:

  • Detecting and quantifying thermodynamic disequilibria are essential for understanding complex natural systems.
  • Nonequilibrium thermodynamics often uses coarse-graining to handle partial information, mimicking experimental constraints.
  • Multifilar events, arising from multiple underlying Markov process transitions, represent a common yet underexplored scenario.

Purpose of the Study:

  • To develop model-free tools for assessing non-Markovian statistics of multifilar events.
  • To provide methods for detecting nonequilibrium behavior and estimating entropy production in complex systems.
  • To relax assumptions in existing frameworks to better analyze multifilar events.

Main Methods:

  • Relaxing a key assumption in a first-passage problem framework to analyze multifilar events.
  • Utilizing the asymmetry of event distributions and waiting times.
  • Employing analytical and numerical illustrations on reference models.

Main Results:

  • Introduction of model-free tools to detect nonequilibrium behavior.
  • Estimation of entropy production rates from multifilar events.
  • Discussion of the tools' applicability across different systems and regimes, providing varying degrees of information from none to exact entropy production values.

Conclusions:

  • The developed tools offer a versatile approach to studying nonequilibrium thermodynamics in systems with multifilar events.
  • These methods are valuable for inferring thermodynamic properties even under experimental limitations and partial information.
  • The findings contribute to a deeper understanding of entropy production in complex, potentially non-Markovian processes.