Fokker-Planck Convergence Analyzer

About

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

• Gibbs distribution convergence verification via likelihood ratio tests
• Mixing time (τ_mix) estimation using Hessian eigenvalue analysis
• 2 GitHub stars
• Temperature sensitivity analysis for noise-controlled training
• Real-time KL divergence tracking between empirical and theoretical states
• Steady-state validation for Langevin-based SDE solvers

Use Cases

• Optimizing training duration by calculating the theoretical mixing time of the loss landscape
• Determining if a Bayesian neural network has reached a true posterior distribution
• Validating the stability and equilibrium of Active Inference agents in complex environments