Isbn: 9781009709033 - generative ai and stochastic thermodynamics: a tale of free energies (16 risultati)

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  • Lingua: Inglese

    Editore: Cambridge University Press 8/20/2026, 2026

    1009709038 / 9781009709033

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    Paperback or Softback. Condizione: New. Generative AI and Stochastic Thermodynamics. Book.

  • Lingua: Inglese

    Editore: Cambridge University Press, 2026

    1009709038 / 9781009709033

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    Da: California Books, Miami, FL, U.S.A.California Books

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    Lingua: Inglese

    Editore: Cambridge University Press, GB, 2026

    1009709038 / 9781009709033

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    Paperback. Condizione: 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.…

  • Lingua: Inglese

    Editore: Cambridge University Press, GB, 2026

    1009709038 / 9781009709033

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    Paperback. Condizione: 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.…

  • Lingua: Inglese

    Editore: Cambridge University Press, 2026

    1009709038 / 9781009709033

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    Condizione: New. 2026. paperback. . . . . .

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    Paperback. Condizione: Brand New. 307 pages. 6.69x0.66x9.61 inches. In Stock.

  • Lingua: Inglese

    Editore: Cambridge University Press, 2026

    1009709038 / 9781009709033

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  • Lingua: Inglese

    Editore: Cambridge University Press, 2026

    1009709038 / 9781009709033

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    Condizione: New. 2026. paperback. . . . . . Books ship from the US and Ireland.

  • Lingua: Inglese

    Editore: Cambridge University Press, GB, 2026

    1009709038 / 9781009709033

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    Paperback. Condizione: 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.…

  • Lingua: Inglese

    Editore: Cambridge University Press Aug 2026, 2026

    1009709038 / 9781009709033

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    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    Taschenbuch. Condizione: Neu. Neuware - 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.…

  • Lingua: Inglese

    Editore: Cambridge University Press, GB, 2026

    1009709038 / 9781009709033

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    Paperback. Condizione: 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.…

  • Lingua: Inglese

    Editore: Cambridge University Press, Cambridge, 2026

    1009709038 / 9781009709033

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    Paperback. Condizione: 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.…

  • Lingua: Inglese

    Editore: Cambridge University Press, 2026

    1009709038 / 9781009709033

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  • Lingua: Inglese

    Editore: Cambridge University Press, 2026

    1009709038 / 9781009709033

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  • Lingua: Inglese

    Editore: Cambridge University Press, Cambridge, 2026

    1009709038 / 9781009709033

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    Paperback. Condizione: 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

  • Lingua: Inglese

    Editore: Cambridge University Press, Cambridge, 2026

    1009709038 / 9781009709033

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    Paperback. Condizione: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…