Data-driven science and engineering
Data-driven science and engineering
- 4th printing
- Cambridge University Press, 2019
- xxii, 472 p. ill. 26 cm.
machine learning, dynamical systems, and control
Part I - Dimensionality Reduction and Transforms 1 - Singular Value Decomposition (SVD)-- 2 - Fourier and Wavelet Transforms-- 3 - Sparsity and Compressed Sensing-- -- Part II - Machine Learning and Data Analysis-- 4 - Regression and Model Selection-- 5 - Clustering and Classification-- 6 - Neural Networks and Deep Learning-- -- Part III - Dynamics and Control-- 7 - Data-Driven Dynamical Systems-- 8 - Linear Control Theory-- 9 - Balanced Models for Control-- 10 - Data-Driven Control-- -- Part IV - Reduced Order Models-- 11 - Reduced Order Models (ROMs)-- 12 - Interpolation for Parametric ROMs--
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.
9781108422093
TA ENGINEERING (GENERAL)
Engineering -- data processing
Mathematical analysis
Machine learning
Dynamical systems
machine learning, dynamical systems, and control
Part I - Dimensionality Reduction and Transforms 1 - Singular Value Decomposition (SVD)-- 2 - Fourier and Wavelet Transforms-- 3 - Sparsity and Compressed Sensing-- -- Part II - Machine Learning and Data Analysis-- 4 - Regression and Model Selection-- 5 - Clustering and Classification-- 6 - Neural Networks and Deep Learning-- -- Part III - Dynamics and Control-- 7 - Data-Driven Dynamical Systems-- 8 - Linear Control Theory-- 9 - Balanced Models for Control-- 10 - Data-Driven Control-- -- Part IV - Reduced Order Models-- 11 - Reduced Order Models (ROMs)-- 12 - Interpolation for Parametric ROMs--
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.
9781108422093
TA ENGINEERING (GENERAL)
Engineering -- data processing
Mathematical analysis
Machine learning
Dynamical systems
