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Tensor-Based Dynamical Systems [electronic resource] : Theory and Applications / by Can Chen.

By: Contributor(s): Material type: TextSeries: Synthesis Lectures on Mathematics & StatisticsPublisher: Cham : Springer International Publishing : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XV, 106 p. 19 illus., 17 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783031545054
Subject(s):
Additional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleLOC classification:
  • TA352-356
  • QC20.7.N6
Online resources:
Contents:
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.
In: Springer Nature eBookSummary: 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.
Item type: List(s) this item appears in: Titluri cărți electronice Springer Nature 2025 (10631 titluri)
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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.

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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