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Peridynamic Differential Operator for Numerical Analysis 2019 edition
Erdogan Madenci
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Peridynamic Differential Operator for Numerical Analysis 2019 edition
Erdogan Madenci
PD also serves as a natural filter to smooth noisy data and to recover missing data. This book starts with an overview of the PD concept, the derivation of the PD differential operator, its numerical implementation for the spatial and temporal derivatives, and the description of sources of error.
282 pages, 137 Illustrations, color; 26 Illustrations, black and white; XI, 282 p. 163 illus., 137 i
| Media | Books Hardcover Book (Book with hard spine and cover) |
| Released | March 4, 2019 |
| ISBN13 | 9783030026462 |
| Publishers | Springer Nature Switzerland AG |
| Pages | 282 |
| Dimensions | 612 g |
| Language | German |
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