Entropy production in chemical reaction networks shows generic critical exponents at pitchfork, transcritical, saddle-node, and Hopf bifurcations, with the inequality α - 2β ≥ 0 implying divergent responses require divergent fluctuations but not conversely.
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Multi-time correlations of state observations are combined via a reconstruction operation into a hierarchy of successively tighter lower bounds on entropy production rate that converge to the true value with dense sampling.
Entropy production hierarchy under Markovian embedding of generalized Langevin dynamics enables non-Markovian extensions of key thermodynamic trade-off relations, with memory allowing finite currents at near-zero dissipation in structured underdamped baths.
Data-driven framework using short-time TUR inference and deep neural networks reconstructs high-dimensional dissipative force fields and localizes fluctuating entropy production in space and time.
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Universal criticality of entropy production in chemical reaction networks
Entropy production in chemical reaction networks shows generic critical exponents at pitchfork, transcritical, saddle-node, and Hopf bifurcations, with the inequality α - 2β ≥ 0 implying divergent responses require divergent fluctuations but not conversely.
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Hierarchical Reconstruction of Time-arrow from Multi-time Correlations
Multi-time correlations of state observations are combined via a reconstruction operation into a hierarchy of successively tighter lower bounds on entropy production rate that converge to the true value with dense sampling.
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Hierarchy of entropy production and thermodynamic trade-off relations in non-Markovian systems
Entropy production hierarchy under Markovian embedding of generalized Langevin dynamics enables non-Markovian extensions of key thermodynamic trade-off relations, with memory allowing finite currents at near-zero dissipation in structured underdamped baths.
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Localizing entropy production along non-equilibrium trajectories
Data-driven framework using short-time TUR inference and deep neural networks reconstructs high-dimensional dissipative force fields and localizes fluctuating entropy production in space and time.