- We will send in 10–14 business days.
/images/books/2819735/9780128167984.jpg)
- Author: Zhendong Luo
- Publisher: Academic Press
- ISBN-10: 012816798X
- ISBN-13: 9780128167984
- Format: 15.2 x 22.9 x 1.5 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Proper Orthogonal Decomposition Methods for Partial Differential Equations (e-book) (used book) | bookbook.eu
Reviews
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 18d.03:11:31
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Zhendong Luo
- Publisher: Academic Press
- ISBN-10: 012816798X
- ISBN-13: 9780128167984
- Format: 15.2 x 22.9 x 1.5 cm, softcover
- Language: English English
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.
Reviews