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.

part I|60 pages


part II|114 pages


chapter Chapter 4|21 pages

The Drug-Induced Protein Folding Problem

Metamodels for Dynamic Targeting

chapter Chapter 5|25 pages

Targeting Protein Structure in the Absence of Structure

Metamodels for Biomedical Applications

chapter Chapter 6|18 pages

Autoencoder as Quantum Metamodel of Gravity

Toward an AI-Based Cosmological Technology

chapter |12 pages