Tensor-Based Dynamical Systems

Theory and Applications

Can Chen

  • Springer

  • Date de publication : 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.


Détails
Auteur
Can  Chen
Marque
Springer
Collection
n.c
Parution
2024-03-04
Pages
106 pages
EAN papier
9783031545047
Langue
Anglais
Informations ebook
EAN PDF
9783031545054
Prix
64,19 €
En savoir plus
Nb pages copiables 1
Nb pages imprimables 10
Taille du fichier 3727 Ko
Ce livre est accessible aux handicaps   Informations sur l'accessibilité
EAN EPUB
9783031545054
Prix
64,19 €
En savoir plus
Nb pages copiables 1
Nb pages imprimables 10
Taille du fichier 9503 Ko
Ce livre est accessible aux handicaps   Informations sur l'accessibilité
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Auteur(s) du livre


Defaut-Illustration 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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