Online local learning for generative thermodynamic computing
Generative thermodynamic computers turn thermal noise into structured data through Langevin dynamics. We train these systems with a local update at each integration step. The reverse-path Onsager-Machlup objective yields a coupling gradient that is a symmetric sum of local residual-state correlations. We apply this gradient immediately rather than accumulating it over a full trajectory. In digital simulations using MNIST prototypes, online and trajectory-batch training reach similar validation losses on fixed noising paths. Models trained online release less heat on average in all five independently seeded pairs, with both models' parameters held fixed during sampling. Auxiliary classifie...
| Authors | Huilin Wang, Weibing Deng |
|---|---|
| Primary category | physics.data-an |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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