Timescale coalescence in AR(1) hidden drivers creates a spectrally dark regime where persistent forcing is invisible to one-pole models, with the local spectral distance scaling as C lambda^4 due to manifold geometry.
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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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Timescale Coalescence Makes Hidden Persistent Forcing Spectrally Dark
Timescale coalescence in AR(1) hidden drivers creates a spectrally dark regime where persistent forcing is invisible to one-pole models, with the local spectral distance scaling as C lambda^4 due to manifold geometry.
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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.