Free-Energy Differences from Nonequilibrium Fluctuations in High Dissipation

What the math gives to ML

The paper’s transferable mechanism is to infer nonequilibrium entropy production from repeated trajectory fluctuations rather than relying on exponentially rare negative-dissipation trajectories required by Jarzynski/Crooks estimators. Neural-network training can be viewed as a stochastic path generated by an optimizer, minibatch sequence, and injected noise, with a reverse path constructed by replaying these ingredients in reverse. Estimating forward/reverse path asymmetry gives a dissipation monitor that can trigger learning-rate reduction before irreversible, unstable updates dominate. The most direct transfer is therefore a fluctuation-based adaptive optimizer controller, not direct use of an exponential work average.