Mathematics for AI and Machine Learning

Foundations for modern AI and machine learning

Fokker-Planck Equation

Chapter 21 Dynamics & diffusion

Chapter 21: Fokker-Planck and Distribution Dynamics — Fokker-Planck Equation

From the book

Chapter 21: Fokker-Planck and Distribution Dynamics. In the chapter mind map this icon labels Fokker-Planck Equation: Drift & Diffusion Terms. The discussion below is excerpted and lightly edited from § Stationary Solution of the Langevin Fokker-Planck Equation in Mathematics for AI and Machine Learning.

When the distribution reaches the target distribution ($p_t = p$), the drift divergence becomes:

Substituting $p_t = p$ into the Fokker-Planck equation yields:

This confirms that $p$ is indeed a stationary (equilibrium) solution of the Langevin Fokker-Planck equation.

What this drawing shows

What you see. Shows $p(x,t)$ evolving under Fokker--Planck dynamics: gray dashed $p_0$ fixed while blue density drifts right and spreads (diffusion); red arrow tracks drift.

In the mind map. Chapter 21 — Fokker-Planck Equation: Drift & Diffusion Terms. See From the book above for definitions, figures, and worked examples.

Where to read next

Open Chapter 21 companion →

Read the full definitions, figures, and worked examples in Chapter 21: Fokker-Planck and Distribution Dynamics — see the mind-map node Fokker-Planck Equation: Drift & Diffusion Terms.

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