Data-Driven Modeling & Scientific Computation by J. Nathan Kutz (.PDF)
File Size: 36.6 MB
Data-Driven Modeling & Scientific Computation : Methods for Complex Systems & Big Data, 2nd Edition by J. Nathan Kutz
Requirements: .PDF reader, 36.6 MB | True PDF
Overview: Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data is an accessible introductory-to-advanced textbook focusing on integrating scientific computing methods and algorithms with modern data analysis techniques, including basic applications of Machine Learning in the sciences and engineering. Its overarching goal is to develop techniques that allow for the integration of the dynamics of complex systems and Big Data. This comprehensive textbook provides a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation, data-driven modelling, and Machine Learning. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological, and physical sciences. The high-level programming language Python is used throughout the book to implement and develop mathematical solution strategies. One specific aim of the book is to integrate standard scientific computing methods with the burgeoning field of data analysis, Machine Learning and Artificial Intelligence (AI). This area of research is expanding at an incredible pace in the sciences due to the proliferation of data collection in almost every field of science.
Genre: Non-Fiction > Tech & Devices
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