Optimal feedback laws and measurement schedules are derived for a mesoscopic information engine with costly binary sensors, recovering the Schmiedl-Seifert protocol in the open-loop limit and revealing deadline-induced blindness plus periodic steady-state schedules.
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A forward-backward HJB duality computes the optimal stochastic transport control from easy forward relaxation trajectories alone, expressed as path-space free energy without backward simulation.
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Optimal Control of a Mesoscopic Information Engine
Optimal feedback laws and measurement schedules are derived for a mesoscopic information engine with costly binary sensors, recovering the Schmiedl-Seifert protocol in the open-loop limit and revealing deadline-induced blindness plus periodic steady-state schedules.
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Generative optimal transport via forward-backward HJB matching
A forward-backward HJB duality computes the optimal stochastic transport control from easy forward relaxation trajectories alone, expressed as path-space free energy without backward simulation.