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Artificial intelligence on dark matter and dark energy = reverse engineering of the Big Bang /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence on dark matter and dark energy/ Ariel Fernández.
Reminder of title:	reverse engineering of the Big Bang /
Author:	Fernández, Ariel.
Published:	Boca Raton, FL :Chapman & Hall/CRC Press, : 2024.,
Description:	1 online resource (xiv, 158 p.)
[NT 15003449]:	1. Conjuring Up Dark Matter and Dark Energy2. Dark Matter in Galaxies: Coming to Grips with an Inevitable Truth3. Dark Energy Is Fueling a Runaway Universe4. An AI Quest for Dark Matter Calls for an Extra Dimension5. Methods: Topological Autoencoders for Dynamical Systems in Molecular to Cosmological Applications6. Querying Artificial Intelligence on Dark Matter and Dark Energy: Quintessential Reverse Engineering of the Standard ModelEpilogue: Conversion of Dark Energy into Dark Matter with Cosmic Reproduction Technology.
Subject:	Big bang theory. -
Online resource:	https://www.taylorfrancis.com/books/9781003385950
ISBN:	9781003385950

Artificial intelligence on dark matter and dark energy = reverse engineering of the Big Bang /
Fernández, Ariel.

Artificial intelligence on dark matter and dark energyreverse engineering of the Big Bang /[electronic resource] :Ariel Fernández. - 1st ed. - Boca Raton, FL :Chapman & Hall/CRC Press,2024. - 1 online resource (xiv, 158 p.) - Chapman & Hall/CRC artificial intelligence and robotics series. - Chapman & Hall/CRC artificial intelligence and robotics series..

Includes bibliographical references and index.

1. Conjuring Up Dark Matter and Dark Energy2. Dark Matter in Galaxies: Coming to Grips with an Inevitable Truth3. Dark Energy Is Fueling a Runaway Universe4. An AI Quest for Dark Matter Calls for an Extra Dimension5. Methods: Topological Autoencoders for Dynamical Systems in Molecular to Cosmological Applications6. Querying Artificial Intelligence on Dark Matter and Dark Energy: Quintessential Reverse Engineering of the Standard ModelEpilogue: Conversion of Dark Energy into Dark Matter with Cosmic Reproduction Technology.

As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold. In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-à-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and - to the best of our knowledge - in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet. This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.

ISBN: 9781003385950Subjects--Topical Terms:

598340
Big bang theory.

LC Class. No.: QB991.B54

Dewey Class. No.: 523.18

Artificial intelligence on dark matter and dark energy = reverse engineering of the Big Bang /
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245 1 0 $a Artificial intelligence on dark matter and dark energy $h [electronic resource] : $b reverse engineering of the Big Bang / $c Ariel Fernández.
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490 1 $a Chapman & Hall/CRC artificial intelligence and robotics series
504 $a Includes bibliographical references and index.
505 0 $a 1. Conjuring Up Dark Matter and Dark Energy2. Dark Matter in Galaxies: Coming to Grips with an Inevitable Truth3. Dark Energy Is Fueling a Runaway Universe4. An AI Quest for Dark Matter Calls for an Extra Dimension5. Methods: Topological Autoencoders for Dynamical Systems in Molecular to Cosmological Applications6. Querying Artificial Intelligence on Dark Matter and Dark Energy: Quintessential Reverse Engineering of the Standard ModelEpilogue: Conversion of Dark Energy into Dark Matter with Cosmic Reproduction Technology.
520 $a As we prod the cosmos at very large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book helps mitigate the crisis. It resorts to artificial intelligence (AI) for answers and describes the outcome of this quest in terms of an ur-universe, a quintessential compact multiply connected space that incorporates a fifth dimension to encode space-time as a latent manifold. In some ways, AI is bolder than humans because the huge corpus of knowledge, starting with the prodigious Standard Model (SM) of particle physics, poses almost no burden to its conjecture-framing processes. Why not feed AI with the SM enriched by the troubling cosmological phenomenology on dark matter and dark energy and see where AI takes us vis-à-vis reconciling the conflicting data with the laws of physics? This is precisely the intellectual adventure described in this book and - to the best of our knowledge - in no other book on the shelf. As the reader will discover, many AI conjectures and validations ultimately make a lot of sense, even if their boldness does not feel altogether "human" yet. This book is written for a broad readership. Prerequisites are minimal, but a background in college math/physics/computer science is desirable. This book does not merely describe what is known about dark matter and dark energy but also provides readers with intellectual tools to engage in a quest for the deepest cosmological mystery.
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650 0 $a Dark matter (Astronomy) $3 862402
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