Popis produktu
Master the future of science and engineering with data!
Data-Driven Science and Engineering is the key to understanding and controlling complex systems using modern machine learning and data science methods. This book guides you through the latest insights that are changing how scientists and engineers approach modeling, prediction, and control.
A comprehensive guide to modern methods
This text integrates innovative machine learning and data science approaches specifically for engineering and scientific communities. You will find new chapters on reinforcement learning and physics-informed machine learning that expand traditional analysis and computational capabilities. The book also includes practical resources such as videos and code available in Python and MATLAB (R), enabling you to apply the methods in practice.
Why choose this book?
Data-Driven Science and Engineering is not just a theoretical overview - it is a tool that equips you with concrete skills to use data in your field and helps you become a leader in modern science and engineering.
Key benefits of the book
- State-of-the-art insights: Integrates machine learning methods with traditional engineering and scientific disciplines.
- Practical applicability: Contains examples and code in Python and MATLAB (R) to facilitate applying the insights.
- New chapters: Reinforcement learning and physics-informed machine learning broaden the range of analytical possibilities.
- Supplementary materials: Video tutorials and code examples help better understand and apply the methods.
- Suited for engineers and scientists: Focused on solving real problems in technical and scientific fields.
What the book offers
- A detailed exposition of data-driven modeling and prediction methods.
- Integration of machine learning and data science in science and engineering applications.
- Practical examples and code for Python and MATLAB (R).
- Chapters on reinforcement learning and physics-informed learning.
- Supporting multimedia materials to facilitate learning and practice.
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