1st Edition

Artificial Intelligence on Dark Matter and Dark Energy Reverse Engineering of the Big Bang

By Ariel Fernández Copyright 2024
    172 Pages 13 Color & 47 B/W Illustrations
    by Chapman & Hall

    172 Pages 13 Color & 47 B/W Illustrations
    by Chapman & Hall

    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.

    1. Conjuring Up Dark Matter and Dark Energy

    2. Dark Matter in Galaxies: Coming to Grips with an Inevitable Truth

    3. Dark Energy Is Fueling a Runaway Universe

    4. An AI Quest for Dark Matter Calls for an Extra Dimension

    5. Methods: Topological Autoencoders for Dynamical Systems in Molecular to Cosmological Applications

    6. Querying Artificial Intelligence on Dark Matter and Dark Energy: Quintessential Reverse Engineering of the Standard Model

    Epilogue: Conversion of Dark Energy into Dark Matter with Cosmic Reproduction Technology


    Ariel Fernández, PhD, (born Ariel Fernández Stigliano) is an Argentine-American physical chemist and mathematician. He earned a PhD in chemical physics at Yale University in record time and held the Hasselmann Endowed Chair Professorship in Bioengineering at Rice University until his retirement. To date, he has published approximately 500 scientific papers in professional journals, including PNAS, Nature, Nature Biotechnology, Physical Review Letters, Genome Research, and Genome Biology. Dr. Fernández has also authored seven books on biophysics, molecular medicine, AI, and mathematical physics, and holds several patents on technological innovation. Since 2018, Dr. Fernández has headed the Daruma Institute for Applied Intelligence, the research arm of AF Innovation, a consultancy currently based in Argentina and the United States.