Fokker-Planck Convergence Analyzer

The Fokker-Planck Analyzer is a specialized validation tool designed to monitor the evolution of probability distributions during neural network training. By applying Fokker-Planck theory to Langevin dynamics, it determines whether empirical weight distributions have reached their theoretical steady state, known as the Gibbs distribution. This skill is essential for identifying if a training process has successfully completed its mixing time or if it remains in a transient phase, providing deep insights into the relationship between loss landscape geometry, temperature, and parameter convergence.

Key Features

Use Cases