Information flow in bipartite Markov systems is split into a housekeeping part that maintains correlations through cyclic modes and an excess part that changes mutual information through conservative forces.
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A unified framework based on generalized means derives an infinite family of thermodynamic speed limits for Markov jump processes and chemical reaction networks, each giving a lower bound on entropy production.
Generalized free energy from large deviations enables excess/housekeeping decomposition of fluxes and dissipation plus thermodynamic speed limits in driven nonequilibrium systems.
A geometric decomposition of entropy production rate in reaction-diffusion systems isolates excess dissipation driving patterns and yields speed limits, uncertainty relations, and an optimal-transport extension for efficient pattern formation.
citing papers explorer
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Geometric decomposition of information flow: New insights into information thermodynamics
Information flow in bipartite Markov systems is split into a housekeeping part that maintains correlations through cyclic modes and an excess part that changes mutual information through conservative forces.
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Infinite variety of thermodynamic speed limits with general activities
A unified framework based on generalized means derives an infinite family of thermodynamic speed limits for Markov jump processes and chemical reaction networks, each giving a lower bound on entropy production.
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Generalized free energy and excess/housekeeping decomposition in nonequilibrium systems: from large deviations to thermodynamic speed limits
Generalized free energy from large deviations enables excess/housekeeping decomposition of fluxes and dissipation plus thermodynamic speed limits in driven nonequilibrium systems.
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Geometric thermodynamics of reaction-diffusion systems: Thermodynamic trade-off relations and optimal transport for pattern formation
A geometric decomposition of entropy production rate in reaction-diffusion systems isolates excess dissipation driving patterns and yields speed limits, uncertainty relations, and an optimal-transport extension for efficient pattern formation.