9780128167984-012816798X-Proper Orthogonal Decomposition Methods for Partial Differential Equations (Mathematics in Science and Engineering)

Proper Orthogonal Decomposition Methods for Partial Differential Equations (Mathematics in Science and Engineering)

ISBN-13: 9780128167984
ISBN-10: 012816798X
Edition: 1
Author: Goong Chen, Zhendong Luo
Publication date: 2018
Publisher: Academic Press
Format: Paperback 278 pages
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Book details

ISBN-13: 9780128167984
ISBN-10: 012816798X
Edition: 1
Author: Goong Chen, Zhendong Luo
Publication date: 2018
Publisher: Academic Press
Format: Paperback 278 pages

Summary

Proper Orthogonal Decomposition Methods for Partial Differential Equations (Mathematics in Science and Engineering) (ISBN-13: 9780128167984 and ISBN-10: 012816798X), written by authors Goong Chen, Zhendong Luo, was published by Academic Press in 2018. With an overall rating of 4.1 stars, it's a notable title among other Applied (Mathematics) books. You can easily purchase or rent Proper Orthogonal Decomposition Methods for Partial Differential Equations (Mathematics in Science and Engineering) (Paperback) from BooksRun, along with many other new and used Applied books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

Proper Orthogonal Decomposition Methods for Partial Differential Equations evaluates the potential applications of POD reduced-order numerical methods in increasing computational efficiency, decreasing calculating load and alleviating the accumulation of truncation error in the computational process. Introduces the foundations of finite-differences, finite-elements and finite-volume-elements. Models of time-dependent PDEs are presented, with detailed numerical procedures, implementation and error analysis. Output numerical data are plotted in graphics and compared using standard traditional methods. These models contain parabolic, hyperbolic and nonlinear systems of PDEs, suitable for the user to learn and adapt methods to their own R&D problems.Explains ways to reduce order for PDEs by means of the POD method so that reduced-order models have few unknownsHelps readers speed up computation and reduce computation load and memory requirements while numerically capturing system characteristicsEnables readers to apply and adapt the methods to solve similar problems for PDEs of hyperbolic, parabolic and nonlinear types
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