Optimal Control of Periodic Nonequilibrium Mechanochemical Systems via Automatic Differentiation

What the math gives to ML

The paper provides a constructive mechanism for optimizing periodically driven nonequilibrium systems: automatic differentiation through a Fokker–Planck solver is used to optimize a control protocol, with low mechanical dissipation achieved by rotating the entire probability distribution at nearly constant angular speed while preserving its shape. This suggests treating a population of neural-network parameters, particles, or latent states as a periodically driven probability density and optimizing the learning-rate/control waveform against a dissipation-like objective rather than only endpoint loss. The most promising transfer is a distributional optimizer whose periodic cycles maintain a nearly rigidly translating parameter distribution, with deviations from constant-speed transport serving as a measurable instability and schedule-adjustment signal.