Topological Dynamics in Metamodel Discovery with Artificial Intelligence: From B

Description: Topological Dynamics in Metamodel Discovery with Artificial Intelligence by Ariel Fernández Dealing with artificial intelligence, this book delineates AIs role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multi-scale hierarchies hitherto considered off limits for data science. FORMAT Hardcover CONDITION Brand New Publisher Description The leveraging of artificial intelligence (AI) for model discovery in dynamical systems is cross-fertilizing and revolutionizing both disciplines, heralding a new era of data-driven science. This book is placed at the forefront of this endeavor, taking model discovery to the next level.Dealing with artificial intelligence, this book delineates AIs role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multiscale hierarchies hitherto considered off limits for data science.Key Features:Introduces new and advanced methods of model discovery for time series data using artificial intelligenceImplements topological approaches to distill "machine-intuitive" models from complex dynamics dataIntroduces a new paradigm for a parsimonious model of a dynamical system without resorting to differential equationsHeralds a new era in data-driven science and engineering based on the operational concept of "computational intuition"Intended for graduate students, researchers, and practitioners interested in dynamical systems empowered by AI or machine learning and in their biological, engineering, and biomedical applications, this book will represent a significant educational resource for people engaged in AI-related cross-disciplinary projects. Author Biography Ariel Fernández is an Argentine-American physical chemist and mathematician. He obtained a Ph. D. degree in Chemical Physics from Yale University and held the Hasselmann Endowed Chair Professorship in Bioengineering at Rice University until his retirement. To date, he has published over 400 scientific papers in professional journals including PNAS, Nature, Nature Biotechnology, Physical Review Letters, Genome Research and Genome Biology. Fernández has also authored five books on biophysics and molecular medicine and holds several patents on technological innovation. Since 2018 Fernández heads the Daruma Institute for Applied Intelligence, the research arm of AF Innovation, a Consultancy based in Argentina and the USA. Table of Contents Preface. About the Author. Part I Fundamentals. Chapter 1 Artificial Intelligence and Dynamical Systems. Chapter 2 Topological Methods for Metamodel Discovery with Artificial Intelligence. Part II Applications. Chapter 3 Artificial Intelligence Reverse-Engineers In Vivo Protein Folding. Chapter 4 The Drug-Induced Protein Folding Problem: Metamodels for Dynamic Targeting. Chapter 5 Targeting Protein Structure in the Absence of Structure: Metamodels for Biomedical Applications. Chapter 6 Autoencoder as Quantum Metamodel of Gravity: Toward an AI-Based Cosmological Technology. Epilogue. Appendix. INDEX. Long Description The leveraging of artificial intelligence (AI) for model discovery in dynamical systems is cross-fertilizing and revolutionizing both disciplines, heralding a new era of data-driven science. This book is placed at the forefront of this endeavour, taking model discovery to the next level. Dealing with artificial intelligence, this book delineates AIs role in model discovery for dynamical systems. With the implementation of topological methods to construct metamodels, it engages with levels of complexity and multi-scale hierarchies hitherto considered off limits for data science. Key Features: Introduces new and advanced methods of model discovery for time-series data using artificial intelligence. Implements topological approaches to distill machine-intuitive models from complex dynamics data. Introduces a new paradigm for a parsimonious model of a dynamical system without resorting to differential equations. Heralds a new era in data-driven science and engineering based on the operational concept of computational intuition. Intended for graduate students, researchers and practitioners interested in dynamical systems empowered by AI, or machine learning and in their biological, engineering and biomedical applications , this book will represent a significant educational resource for people engaged in AI-related cross-disciplinary projects. Details ISBN103236632X Publisher Taylor & Francis Ltd Year 2022 ISBN-10 103236632X ISBN-13 9781032366326 Format Hardcover Publication Date 2022-12-21 UK Release Date 2022-12-21 Place of Publication London Country of Publication United Kingdom AU Release Date 2022-12-21 NZ Release Date 2022-12-21 Pages 210 Illustrations 13 Line drawings, color; 58 Line drawings, black and white; 3 Halftones, color; 15 Halftones, black and white; 16 Illustrations, color; 73 Illustrations, black and white Subtitle From Biomedical to Cosmological Technologies Author Ariel Fernández Series Chapman & Hall/CRC Artificial Intelligence and Robotics Series DEWEY 514.3082563 Audience Professional & Vocational Imprint Chapman & Hall/CRC Alternative 9781032366333 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:141356998;

Price: 189 AUD

Location: Melbourne

End Time: 2025-01-10T03:35:47.000Z

Shipping Cost: 0 AUD