Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
by Steven L. Brunton, J. Nathan Kutz
Data-Driven Science and Engineering by Steven L. Brunton and J. Nathan Kutz delves into machine learning, dynamical systems, and control. This book equips readers with essential tools for data-centric scientific computing and engineering applications, bridging theory and practice in modern research.
About This Book
Data-Driven Science and Engineering introduces key concepts at the forefront of modern scientific computing. The book focuses on machine learning techniques applied to dynamical systems and control problems.
Authors Steven L. Brunton and J. Nathan Kutz provide a structured approach to leveraging data for engineering solutions. It covers foundational principles that bridge theoretical models with real-world applications.
Designed for researchers and practitioners, the text emphasizes computational methods to handle large datasets in scientific contexts. It serves as a resource for advancing data-centric approaches in engineering disciplines.
The content integrates interdisciplinary tools to address challenges in system analysis and design. Readers gain insights into how data informs predictive modeling and optimization strategies.
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