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Generative AI Through Stochastic Dynamics: A Hands-On Guide to Stochastic Thermodynamics, Diffusion Models, Langevin Dynamics, SDEs, Score Matching, F
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Publisher: Independently Published
Pages: 300
Weight: 1.54
Height: 0.63 Width: 8.50 Depth: 11.00
Language: English
Modern generative AI is powerful-but understanding why diffusion models, score-based generative models, stochastic differential equations, and flow matching actually work is another challenge entirely.
You may know how to train a neural network or follow a diffusion models PyTorch tutorial, yet still wonder: Why does adding noise create a generative process? Where does the reverse-time SDE come from? What does a score function represent? How are Langevin dynamics, stochastic thermodynamics, machine learning, diffusion, and flow matching connected?
Generative AI Through Stochastic Dynamics bridges that gap.
Rather than treating modern generative models as disconnected architectures, this hands-on guide develops them through one powerful idea: generative AI as the transformation of probability distributions through stochastic and deterministic dynamics. You will build the generative AI mathematics needed to understand how probability, energy, noise, scores, and velocity fields become practical algorithms.
From probability and Brownian motion, you will progress through Langevin dynamics, stochastic differential equations in machine learning, Fokker-Planck equations, diffusion models, score-based generative models, optimal transport, normalizing flows, flow matching generative models, stochastic control, and complete generative systems.
Inside, you will learn how to:
Build strong foundations in generative AI mathematics, probability, entropy, KL divergence, Brownian motion, and stochastic dynamics
Understand stochastic thermodynamics for machine learning, including energy, free energy, equilibrium, nonequilibrium dynamics, and entropy production
Derive and simulate Langevin dynamics and stochastic differential equations (SDEs)
Understand diffusion models from first principles and build a diffusion model in PyTorch from scratch
Master score-based generative models, score matching, VP and VE processes, reverse-time SDEs, and probability-flow ODEs
Explore normalizing flows for machine learning, neural ODEs, optimal transport, flow matching, and conditional flow matching
Connect VAEs, energy-based models, MCMC, Langevin sampling, stochastic control, and Schrödinger bridges
Build reproducible Python and PyTorch experiments and evaluate distribution quality, numerical accuracy, NFE, latency, and sampling performance
Diagnose mathematical, numerical, training, and sampling failures instead of relying on trial and error
A defining feature of the book is its careful treatment of stochastic thermodynamics and machine learning. You will explore rigorous connections involving probability currents, stochastic paths, time reversal, entropy, energy, and nonequilibrium dynamics while learning to distinguish genuine physical thermodynamics from mathematical analogies used in generative computation.
Whether you are an advanced student, machine-learning engineer, AI researcher, computational scientist, or applied mathematician, this book gives you a deeper framework for understanding the mathematics and computation behind modern generative AI.
Stop treating diffusion models, scores, flows, and SDEs as isolated techniques. Learn the principles that connect them-and build generative models from the mathematics upward.
Understand the physics. Derive the mathematics. Implement the dynamics. Build the generative models.
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