Topological Dynamics in Metamodel Discovery with Artificial Intelligence From Biomedical to Cosmological Technologies

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 AI’s 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 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.

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.

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.