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.
Author(s): Ariel Fernández
Series: Chapman & Hall/CRC Artificial Intelligence and Robotics Series
Publisher: CRC Press
Year: 2023
Language: English
Pages: 172
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Author
Chapter 1 Conjuring Up Dark Matter and Dark Energy
Summary
1.1 Why Dark Matter and Dark Energy?
1.2 Querying AI on Dark Matter and Dark Energy
References
Chapter 2 Dark Matter in Galaxies: Coming to Grips with an Inevitable Truth
Summary
2.1 Dark Matter in Galaxies and Anomalies in Star Rotation
2.2 Gravitational Lenses as Dark Matter Detectors
2.2.1 Why Did Einstein Claim That Gravity Influences Light?
2.2.2 The Physics of Gravitational Lensing as a Dark Matter Detector
2.3 The Dark Matter Hypothesis is Upheld
References
Chapter 3 Dark Energy Is Fueling a Runaway Universe
Summary
3.1 An Expanding Universe in the Classical Newtonian World: The Troubled Origin of a Relativistic Equation
3.2 Physical Picture of an Expanding Universe from the Big Bang until Today
3.3 The Universe in a Runaway Mode
3.4 The Universe Runaway as Fueled by Dark Energy
3.5 How Could the Creation of Vacuum Trigger the Universe Runaway? Quantum Mechanics Underpinnings of the Anomalous Behavior
References
Chapter 4 An AI Quest for Dark Matter Calls for an Extra Dimension
Summary
4.1 Gravity, Dark Matter, and Extra Dimensions
4.2 Elementary Particles as Warps in Quantum Fields
4.3 Allowed Topology of the Universe Admitting a CUrled-Up Extra Dimension
4.4 A Topological Metamodel of the Universe in the AI Quest for Dark Matter
References
Chapter 5 Methods: Topological Autoencoders for Dynamical Systems in Molecular to Cosmological Applications
Summary
5.1 Primer on Neural Networks for Model Discovery
5.2 Neural Networks as Dynamical Systems
5.3 Deep Learning and Convolutional Neural Networks for Feature Extraction
5.4 Metamodels for Adiabatic Regimes, Latent Manifolds, and Quotient Spaces
5.5 The Standard Model as a Dynamical System
5.6 AI-Based Metamodel Discovery for Dynamical Systems
5.7 Autoencoders in the Quest for Latent Coordinates
5.8 Model Discovery with Deep Learning
5.9 Extending Molecular Dynamics with Variational Autoencoders
5.10 Metamodels on Latent Manifolds
5.11 Quotient Spaces for Dynamical Systems
5.12 Molecular Dynamics Encoded and Propagated as Topological Dynamics
5.13 Autoencoder Batteries for Hierarchical Systems
5.14 Decoding Quantum Gravity
5.15 Quantum Gravity Autoencoder
5.16 Relativistic Strings in the Quantum Physics of Machine Learning
5.17 The Universe as a Holographic Autoencoder
5.18 Cosmological Technology Using Quantum Gravity Autoencoders
References
Chapter 6 Querying Artificial Intelligence on Dark Matter and Dark Energy: Quintessential Reverse Engineering of the Standard Model
Summary
6.1 A Quintessential Autoencoder to Decode the Standard Model
6.2 AI-Enabled Reverse Engineering of the Standard Model through Quintessential Decoding of an Early Universe
6.3 Validating the Quintessential Decoding of Elementary Particles as Ur-Particles
6.4 Quintessential Geometric Dilution Steers the Universe’s Evolution after the Big Bang
6.5 Dark Matter and Dark Energy Identified by the Quintessential Autoencoder of the Standard Model
6.6 Generating Dark Matter and Dark Energy in the Holographic Autoencoder with Geometric Dilution as a Proxy for Time
6.7 The Universe in the Quintessential Reverse Autoencoder: No Extension of the Standard Model May Yield Dark Energy or Dark Matter
References
Epilogue: Conversion of Dark Energy into Dark Matter with Cosmic Reproduction Technology
E.1 The Cosmological Constant Problem in a Multiverse Matrix
E.2 Relativity Made “Physical” – The Imprint of the Universe-Simulating Processor
References
Appendix
A.1 Model Discovery with Autoencoders and Transformers: In Vivo Physics Applications
A.1.1 Transformer Autoencoders for In Vivo Molecular Contexts
A.1.2 Transformers Empowering Molecular Dynamics
A.1.2.1 Propagating the Topological Dynamics with Transformers
A.1.2.2 Topological Metamodeling
A.1.3 Folding Proteins with Transformers
A.1.4 Artificial Intelligence Recreates In Vivo Reality
A.1.5 Propagating In Vitro Dynamics with Autoencoders
A.1.6 Short Atomistic Simulation of In Vivo Protein Folding
A.1.7 In Vivo Folding Trajectories Generated with Transformer Technology
A.1.8 AI Generates In Vivo Folding Pathways
A.1.9 Reverse-Engineering of the Expeditious In Vivo Context I: Iterative Annealing in the Chaperone Chamber
A.1.10 Reverse-Engineering of the In Vivo Context II: The GroEL Chamber in the (ATP)7 State
A.1.11 Can AI Truly Handle In Vivo Molecular Reality?
References
A.2 Wave Functions for Ur-Particles that Decode Elementary Particles in the Standard Model
Index
