Tensor-Based Dynamical Systems
Theory and Applications
1st Edition
Published on 4. March 2024
XV, 106 pages
978-3-031-54505-4 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XV, 106 p. 19 illus., 17 illus. in color.
File size
3,73 MB
ISBN-13
978-3-031-54505-4 (9783031545054)
DOI
10.1007/978-3-031-54505-4
Schweitzer Classification
Other editions
Additional editions
Book
03/2024
Springer
€85.59
Shipment within 15-20 days
Person
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.
Content
Tensors and Tensor Algebra.- Tucker Product Representation.- Einstein Product Representation.- CP/tensor Train Decomposition Representation.- Tensor Vector Product Representation.- Contract Product Representation.- T-product Representation.
