Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

Price: $64.99 – $61.18
(as of Dec 18,2024 10:14:40 UTC – Details)

Publisher ‏ : ‎ Cambridge University Press; 2nd edition (July 28, 2022)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 614 pages
ISBN-10 ‏ : ‎ 1009098489
ISBN-13 ‏ : ‎ 978-1009098489
Item Weight ‏ : ‎ 3.05 pounds
Dimensions ‏ : ‎ 7 x 1.25 x 10 inches

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

In today’s rapidly advancing technological landscape, data-driven approaches have become increasingly essential in the fields of science and engineering. Machine learning, dynamical systems, and control are three key areas where data-driven methods are revolutionizing how we analyze and understand complex systems.

Machine learning algorithms, such as neural networks and deep learning models, have proven to be powerful tools for extracting patterns and insights from large datasets. These algorithms can be applied to a wide range of scientific and engineering problems, from predicting weather patterns to optimizing manufacturing processes.

Dynamical systems theory, on the other hand, focuses on understanding the behavior of systems that evolve over time. By analyzing the underlying dynamics of a system, researchers can make predictions and design control strategies to influence its behavior. Data-driven approaches in dynamical systems can help uncover hidden patterns and relationships that traditional methods may overlook.

Control theory, meanwhile, deals with the design of systems that can automatically adjust their behavior to achieve desired outcomes. By incorporating data-driven techniques, such as reinforcement learning and adaptive control, engineers can create more efficient and robust control systems that can adapt to changing environments and uncertainties.

Overall, the integration of machine learning, dynamical systems, and control in data-driven science and engineering is transforming how we approach complex problems. By leveraging the power of data and advanced algorithms, researchers and engineers can gain deeper insights, make more accurate predictions, and design more effective control strategies. The future of science and engineering lies in harnessing the potential of data-driven approaches to unlock new possibilities and drive innovation.
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