Overview
- Introduces key concepts of Mechanistic Data Science for decision making and problem solving
- Demonstrates innovative solutions of engineering problems by combining data science and mechanistic knowledge
- Reinforce concepts with forensic engineering examples
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book introduces Mechanistic Data Science (MDS) as a structured methodology for combining data science tools with mathematical scientific principles (i.e., “mechanistic” principles) to solve intractable problems. Traditional data science methodologies require copious quantities of data to show a reliable pattern, but the amount of required data can be greatly reduced by considering the mathematical science principles. MDS is presented here in six easy-to-follow modules: 1) Multimodal data generation and collection, 2) extraction of mechanistic features, 3) knowledge-driven dimension reduction, 4) reduced order surrogate models, 5) deep learning for regression and classification, and 6) system and design. These data science and mechanistic analysis steps are presented in an intuitive manner that emphasizes practical concepts for solving engineering problems as well as real-life problems. This book is written in a spectral style and is ideal as an entry leveltextbook for engineering and data science undergraduate and graduate students, practicing scientists and engineers, as well as STEM (Science, Technology, Engineering, Mathematics) high school students and teachers.
Authors and Affiliations
About the authors
Dr. Wing Kam Liu is Walter P. Murphy Professor of Mechanical Engineering & Civil and Environmental Engineering and (by courtesy) Materials Science and Engineering, and Director of Global Center on Advanced Material Systems and Simulation (CAMSIM) at Northwestern University in Evanston, Illinois; Dr. Zhengtao Gan is Research Assistant Professor in the Department of Mechanical Engineering at Northwestern University in Evanston, Illinois; and Dr. Mark Fleming, is the Chief Technical Officer of Fusion Engineering, and an Adjunct Professor in the Department of Mechanical Engineering at Northwestern University in Evanston, Illinois.
Bibliographic Information
Book Title: Mechanistic Data Science for STEM Education and Applications
Authors: Wing Kam Liu, Zhengtao Gan, Mark Fleming
DOI: https://doi.org/10.1007/978-3-030-87832-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-87831-3Published: 22 December 2021
Softcover ISBN: 978-3-030-87834-4Published: 23 December 2022
eBook ISBN: 978-3-030-87832-0Published: 01 January 2022
Edition Number: 1
Number of Pages: XV, 276
Number of Illustrations: 23 b/w illustrations, 181 illustrations in colour
Topics: Engineering Mathematics, Statistics, general, Computational Intelligence, Engineering Design