Generative AI and Stochastic Thermodynamics

A timely text providing a unifying perspective on the mathematics of generative AI and stochastic thermodynamics.

Bridging the gap between stochastic thermodynamics and generative AI, this book will interest those working in either discipline, as well as physicists hoping to enter the field of AI. It covers the fundamental concepts before progressing to more advanced methods and encourages the reader to build their intuition.Originating from lectures delivered at the African Institute of Mathematical Sciences, this book presents a unifying perspective on traditional and modern methods in generative AI and stochastic thermodynamics. By relating the core topics in machine learning to the notion of (variational) free-energy, a bridge is built between methods such as latent variable models, variational auto-encoders, optimal control, optimal transport, normalizing flows and diffusion models and concepts such as entropy production and fluctuation theorems in stochastic thermodynamics. Structured into three main parts, the book commences by setting up the required mathematical and statistical physics preliminaries needed to make it broadly accessible. The largest part of the book then focuses on building intuition of major advances in generative AI by considering discrete time processes and their relationship to topics in stochastic thermodynamics. Finally, the authors take a short excursion to the continuous time domain for the more advanced learner.