Data-Driven Science and Engineering 论文
2019Cambridge University Press eBooks引用 1091
Model Reduction and Neural NetworksProbabilistic and Robust Engineering DesignGaussian Processes and Bayesian Inference
详细信息
- 发表期刊/会议
- Cambridge University Press eBooks
- 发表日期
- 2019-01-31
- 发表年份
- 2019
关键词
Model Reduction and Neural NetworksProbabilistic and Robust Engineering DesignGaussian Processes and Bayesian Inference
摘要
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.