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Stochastic thermodynamics provides a universal framework for analyzing nano- and micro-sized non-equilibrium systems. Prominent examples are single molecules, molecular machines, colloidal particles in time-dependent laser traps and biochemical networks. Thermodynamic notions like work, heat
and entropy can be identified on the level of individual fluctuating trajectories. They obey universal relations like the fluctuation theorem.

Thermodynamic inference as a general strategy uses consistency constraints derived from stochastic thermodynamics to infer otherwise hidden properties of non-equilibrium systems. As a paradigm for thermodynamic inference, the thermodynamic uncertainty relation provides a lower bound on the entropy production through measurements of the mean and dispersion of any current in the system. Likewise, it provides a model-free bound on the thermodynamic efficiency of molecular motors. Waiting-time distributions between consecutive transitions in a discrete Markov network yield an even better estimator of entropy production. Moreover, they reveal further information about the topology of the underlying network. From the observation of coherent oscillations, a universal bound on their thermodynamic cost can be deduced.