In stochastic systems with non-diagonal noise and switching environments, mutual information includes irreducible static and dynamic interference terms that prevent simple decomposition.
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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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Emergence of information interference in stochastic systems with non-diagonal noise and switching environments
In stochastic systems with non-diagonal noise and switching environments, mutual information includes irreducible static and dynamic interference terms that prevent simple decomposition.
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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.