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Tensor-Based Dynamical Systems

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Tensor-Based Dynamical Systems

Theory and Applications

Can Chen

Springer
Publication date : 2024-03-04

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This book provides a comprehensive review on tensor algebra, including tensor products, tensor unfolding, tensor eigenvalues, and tensor decompositions. Tensors are multidimensional arrays generalized from vectors and matrices, which can capture higher-order interactions within multiway data. In addition, tensors have wide applications in many domains such as signal processing, machine learning, and data analysis, and the author explores the role of tensors/tensor algebra in tensor-based dynamical systems where system evolutions are captured through various tensor products. The author provides an overview of existing literature on the topic and aims to inspire readers to learn, develop, and apply the framework of tensor-based dynamical systems.

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About the book

Author

Can Chen

Imprint

Springer

Collection

n.c

Publication date

2024-03-04

Pages

106 pages

Print ISBN

9783031545047

Language

English

Ebook informations

EAN PDF

9783031545054

Price

£54.99

Copy count 1

Print count 10

File size 3727 Ko

Informations accessibility

EAN EPUB

9783031545054

Price

£54.99

Copy count 1

Print count 10

File size 9503 Ko

Informations accessibility

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About author(s)

Can Chen

Can Chen, Ph.D. is an Assistant Professor in the School of Data Science and Society with a second appointment in the Department of Mathematics at the University of North Carolina at Chapel Hill. He received the B.S. degree in Mathematics from the University of California, Irvine in 2016, and the M.S. degree in Electrical and Computer Engineering and the Ph.D. degree in Applied and Interdisciplinary Mathematics from the University of Michigan in 2020 and 2021, respectively. He was a Postdoctoral Research Fellow in the Channing Division of Network Medicine at Brigham and Women's Hospital and Harvard Medical School from 2021 to 2023. His research interests span a diverse range of fields, including control theory, network science, tensor algebra, numerical analysis, data science, machine learning, deep learning, hypergraph learning, data analysis, and computational biology.

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