Mechanistic Data Science for STEM Education and Applications / by Wing Kam Liu, Zhengtao Gan, Mark Fleming.

Format:

Book

Author/Creator:

Liu, W. K. (Wing Kam), author.

Fleming, Mark, 1969- author.

Gan, Zhengtao, author.

Series:

Mathematics and Statistics Series

Language:

English

Subjects (All):

Engineering mathematics.

Quantitative research.

Computational intelligence.

Sampling (Statistics).

Engineering design.

Engineering Mathematics.

Data Analysis and Big Data.

Computational Intelligence.

Methodology of Data Collection and Processing.

Engineering Design.

Local Subjects:

Engineering Mathematics.

Data Analysis and Big Data.

Computational Intelligence.

Methodology of Data Collection and Processing.

Engineering Design.

Physical Description:

1 online resource (287 pages)

Edition:

1st ed. 2021.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

Summary:

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 level textbook 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.

Contents:

1-Introduction to Mechanistic Data Science

2-Multimodal Data Generation and Collection

3-Optimization and Regression

4-Extraction of Mechanistic Features

5-Knowledge-Driven Dimension Reduction and Reduced Order Surrogate Models

6-Deep Learning for Regression and Classification

7-System and Design.

Notes:

Includes bibliographical references and index.

Other Format:

Print version: Liu, Wing Kam Mechanistic Data Science for STEM Education and Applications

ISBN:

3-030-87832-5

OCLC:

1292352872