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Most probable paths for active Ornstein-Uhlenbeck particles.

Sandipan Dutta1

  • 1Department of Physics, Birla Institute of Technology and Science, Pilani, Rajasthan, 333031, India.

Physical Review. E
|June 17, 2023
PubMed
Summary
This summary is machine-generated.

We analyzed the most probable paths in nonequilibrium systems, revealing how fluctuations impact entropy production. This research aids in designing artificial active systems with specific trajectories.

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Area of Science:

  • Statistical Mechanics
  • Non-equilibrium Thermodynamics
  • Active Matter Physics

Background:

  • Fluctuations significantly influence stochastic system dynamics, especially in small systems where they cause thermodynamic quantities to deviate from averages.
  • Understanding these deviations is crucial for characterizing non-equilibrium processes.

Purpose of the Study:

  • To analyze the most probable paths (extremum paths) in non-equilibrium systems using the Onsager-Machlup formalism.
  • To investigate how entropy production along these paths differs from the average entropy production.
  • To determine the information about non-equilibrium nature obtainable from these paths and their dependence on system parameters.

Main Methods:

  • Application of the Onsager-Machlup variational formalism.
  • Analysis of active Ornstein-Uhlenbeck particles as a model system.
  • Investigation of path properties concerning persistence time, swim velocity, and active noise.

Main Results:

  • Characterization of the most probable paths in active Ornstein-Uhlenbeck particles.
  • Quantification of the difference between entropy production along most probable paths and average entropy production.
  • Demonstration of how path properties depend on parameters like persistence time, swim velocity, and active noise.

Conclusions:

  • Extremum paths provide insights into the non-equilibrium nature of systems.
  • The study offers a framework for understanding and potentially controlling trajectories in active matter.
  • Findings are applicable to the design of artificial active systems with desired behaviors.