%0 Book %1 brunton2019datadriven %A Brunton, Steven Lee %A Kutz, Jose Nathan %D 2019 %I Cambridge University Press %K 37n35-dynamical-systems-in-control 62h30-classification-discrimination-cluster-analysis 62jxx-linear-inference-regression 93b11-system-structure-simplification 94cxx-circuits-networks %R 10.1017/9781108380690 %T Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control %U https://www.cambridge.org/core/books/datadriven-science-and-engineering/77D52B171B60A496EAFE4DB662ADC36E %X "Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling 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 1108422098

@book{brunton2019datadriven, abstract = {"Data-driven discovery is revolutionizing the modelling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modelling 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"}, added-at = {2020-09-03T05:56:06.000+0200}, author = {Brunton, Steven Lee and Kutz, Jose Nathan}, biburl = {https://www.bibsonomy.org/bibtex/28bdf1ac7f2aaa4fbe6107cc00795fc6d/gdmcbain}, doi = {10.1017/9781108380690}, interhash = {033098a04e038c72e24589baee8cfd2c}, intrahash = {8bdf1ac7f2aaa4fbe6107cc00795fc6d}, isbn = {9781108422093 1108422098}, keywords = {37n35-dynamical-systems-in-control 62h30-classification-discrimination-cluster-analysis 62jxx-linear-inference-regression 93b11-system-structure-simplification 94cxx-circuits-networks}, publisher = {Cambridge University Press}, refid = {1052620258}, timestamp = {2020-09-03T05:58:59.000+0200}, title = {Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control}, url = {https://www.cambridge.org/core/books/datadriven-science-and-engineering/77D52B171B60A496EAFE4DB662ADC36E}, year = 2019 }