As we prod the cosmos at large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book resorts to artificial intelligence for answers and describes the outcome of this quest in terms of an ur-universe.
As we prod the cosmos at large scales, basic tenets of physics seem to crumble under the weight of contradicting evidence. This book resorts to artificial intelligence for answers and describes the outcome of this quest in terms of an ur-universe.
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.
Inhaltsangabe
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.
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.
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