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.
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.