Generative AI and Stochastic Thermodynamics: A Tale of Free Energies - Softcover

Welling, Max

9781009709033: Generative AI and Stochastic Thermodynamics: A Tale of Free Energies

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ISBN 10: 1009709038 ISBN 13: 9781009709033

Publisher: Cambridge University Press, 2026

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

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About the Authors

Max Welling is Full Professor in Machine Learning at the University of Amsterdam. He is co-founder and CTO of the startup CuspAI. Professor Welling is a member of the Dutch Royal Academy of Sciences and was the recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021.

Sirui Lu is Doctoral Researcher at the Max Planck Institute of Quantum Optics, Germany. His research directions are the deep integration between (quantum) physics and artificial intelligence. Previously, he obtained his bachelor's degree from the Department of Physics at Tsinghua University, Beijing.

Lars Holdijk is Ph.D. student at the University of Oxford. His research focusses on the intersection of Generative Artificial Intelligence, Computational Biochemistry and Statistical Physics.

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Publisher: Cambridge University Press
Publication date: 2026
Language: English
ISBN 10: 1009709038
ISBN 13: 9781009709033
Binding: Paperback
Number of pages: 314

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Generative AI and Stochastic Thermodynamics (Paperback or Softback)

Welling, Max

Published by Cambridge University Press 8/20/2026, 2026

ISBN 10: 1009709038 ISBN 13: 9781009709033

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Generative AI and Stochastic Thermodynamics

Welling, Max

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ISBN 10: 1009709038 ISBN 13: 9781009709033

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Generative AI and Stochastic Thermodynamics (Paperback)

Max Welling

Published by Cambridge University Press, Cambridge, 2026

ISBN 10: 1009709038 ISBN 13: 9781009709033

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Paperback. Condition: new. Paperback. 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009709033

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Generative AI and Stochastic Thermodynamics

Lars Holdijk, Max Welling, Sirui Lu

Published by Cambridge University Press, GB, 2026

ISBN 10: 1009709038 ISBN 13: 9781009709033

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Seller: Rarewaves.com USA, London, LONDO, United Kingdom

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Paperback. Condition: New. 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. Seller Inventory # LU-9781009709033

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