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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.
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.
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