Cambridge University Press eBooks· 2019· Book chapter
Data-Driven Science and Engineering
- 1,116citations
- 2019year
Short summary
This textbook integrates machine learning, engineering mathematics, and mathematical physics to enable data-driven modeling and control of complex dynamical systems.
AI-generated from the title and abstract; the full text is not read.
Key points
- Integrates machine learning, engineering mathematics, and mathematical physics.
- Focuses on data-driven modeling and control of dynamical systems.
- Highlights advances in scientific computing for complex systems.
- Covers applications in turbulence, climate, finance, robotics, and more.
AI-generated from the title and abstract; the full text is not read.
Abstract
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
The authors' abstract, as published at the source. Cambridge University Press eBooks, 2019 · DOI ↗
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Field: Statistical and Nonlinear Physics
Statistical and Nonlinear PhysicsPhysics and Astronomy