Mathematical modeling : a dynamical systems approach to analyze practical problems in STEM disciplines / Antonio Palacios.
Palacios, Antonio, author.
2022
TA342
Available Online

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Title Mathematical modeling : a dynamical systems approach to analyze practical problems in STEM disciplines / Antonio Palacios.
Author Palacios, Antonio, author.
ISBN 9783031047299 (electronic bk.)
303104729X (electronic bk.)
9783031047282
3031047281
DOI https://doi.org/10.1007/978-3-031-04729-9
Published Cham : Springer, [2022]
Copyright ©2022
Language English
Description 1 online resource (xvii, 564 pages) : illustrations (some color).
Item Number 10.1007/978-3-031-04729-9 doi
Call Number TA342
Dewey Decimal Classification 620.001/5118
Summary This book provides qualitative and quantitative methods to analyze and better understand phenomena that change in space and time. An innovative approach is to incorporate ideas and methods from dynamical systems and equivariant bifurcation theory to model, analyze and predict the behavior of mathematical models. In addition, real-life data is incorporated in the derivation of certain models. For instance, the model for a fluxgate magnetometer includes experiments in support of the model. The book is intended for interdisciplinary scientists in STEM fields, who might be interested in learning the skills to derive a mathematical representation for explaining the evolution of a real system. Overall, the book could be adapted in undergraduate- and postgraduate-level courses, with students from various STEM fields, including: mathematics, physics, engineering and biology.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Source of Description Online resource; title from PDF title page (SpringerLink, viewed October 4, 2022).
Series Mathematical engineering.
Available in Other Form Print version: 9783031047282
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Record Appears in Online Resources > Ebooks
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Introduction- Algebraic Models
Discrete Models
Continuous Models
Bifurcation Theory
Network-Based Modeling
Delay Models
Spatial-Temporal Models
Stochastic Models
Model Reduction and Simplification.

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