Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
by Steven L. Brunton, J. Nathan Kutz
This book by Steven L. Brunton and J. Nathan Kutz delves into data-driven methods at the intersection of machine learning, dynamical systems, and control. It equips scientists and engineers with tools to model and analyze complex systems using computational techniques. Focused on practical applications, it bridges theory and implementation for innovative problem-solving in dynamic environments.
About This Book
Data-Driven Science and Engineering introduces key concepts at the nexus of machine learning, dynamical systems, and control, providing a foundation for applying data-centric methods to complex problems.
The book covers essential techniques for analyzing and modeling dynamic systems using modern computational tools, emphasizing practical implementation in scientific and engineering contexts.
Authors Steven L. Brunton and J. Nathan Kutz draw on their expertise to bridge theoretical principles with real-world applications, making advanced topics accessible to a broad audience.
Through structured examples and methodologies, readers learn to leverage data for prediction, optimization, and control in diverse fields.
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