Topological Dynamics In Metamodel Discovery With Artificial Intelligence

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Topological Dynamics in Metamodel Discovery with Artificial Intelligence

Topological Dynamics in Metamodel Discovery with Artificial Intelligence
Author :
Publisher : CRC Press
Total Pages : 228
Release :
ISBN-10 : 9781000806427
ISBN-13 : 1000806421
Rating : 4/5 (421 Downloads)

Book Synopsis Topological Dynamics in Metamodel Discovery with Artificial Intelligence by : Ariel Fernández

Download or read book Topological Dynamics in Metamodel Discovery with Artificial Intelligence written by Ariel Fernández and published by CRC Press. This book was released on 2022-12-21 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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