Advances in Peridynamics:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing
2022
Cham Springer |
| Ausgabe: | 1st ed. 2022 |
| Schlagworte: | |
| Online-Zugang: | BFB01 FKE01 TUM01 UBM01 UBT01 UBY01 Volltext |
| Beschreibung: | 1 Online-Ressource (XVI, 422 p. 240 illus., 234 illus. in color) |
| ISBN: | 9783030978587 |
| DOI: | 10.1007/978-3-030-97858-7 |
Internformat
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| 245 | 1 | 0 | |a Advances in Peridynamics |c by Erdogan Madenci, Pranesh Roy, Deepak Behera |
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| 700 | 1 | |a Behera, Deepak |4 aut | |
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Datensatz im Suchindex
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| author | Madenci, Erdogan Roy, Pranesh Behera, Deepak |
| author_facet | Madenci, Erdogan Roy, Pranesh Behera, Deepak |
| author_role | aut aut aut |
| author_sort | Madenci, Erdogan |
| author_variant | e m em p r pr d b db |
| building | Verbundindex |
| bvnumber | BV048251744 |
| classification_tum | PHY 000 |
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| ctrlnum | (ZDB-2-PHA)9783030978587 (OCoLC)1322800539 (DE-599)BVBBV048251744 |
| dewey-full | 530.15 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 530 - Physics |
| dewey-raw | 530.15 |
| dewey-search | 530.15 |
| dewey-sort | 3530.15 |
| dewey-tens | 530 - Physics |
| discipline | Physik |
| discipline_str_mv | Physik |
| doi_str_mv | 10.1007/978-3-030-97858-7 |
| edition | 1st ed. 2022 |
| format | Electronic eBook |
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| id | DE-604.BV048251744 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:57:05Z |
| indexdate | 2024-07-10T09:33:09Z |
| institution | BVB |
| isbn | 9783030978587 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033632061 |
| oclc_num | 1322800539 |
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| owner_facet | DE-91 DE-BY-TUM DE-19 DE-BY-UBM DE-706 DE-522 DE-859 DE-703 |
| physical | 1 Online-Ressource (XVI, 422 p. 240 illus., 234 illus. in color) |
| psigel | ZDB-2-PHA ZDB-2-PHA_2022 |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer International Publishing Springer |
| record_format | marc |
| spelling | Madenci, Erdogan Verfasser aut Advances in Peridynamics by Erdogan Madenci, Pranesh Roy, Deepak Behera 1st ed. 2022 Cham Springer International Publishing 2022 Cham Springer 1 Online-Ressource (XVI, 422 p. 240 illus., 234 illus. in color) txt rdacontent c rdamedia cr rdacarrier Mathematical Physics Engineering Mechanics Engineering Mathematics Applied Dynamical Systems Machine Learning Mathematical physics Mechanics, Applied Engineering mathematics Dynamics Nonlinear theories Machine learning Roy, Pranesh aut Behera, Deepak aut Erscheint auch als Druck-Ausgabe 978-3-030-97857-0 Erscheint auch als Druck-Ausgabe 978-3-030-97859-4 Erscheint auch als Druck-Ausgabe 978-3-030-97860-0 https://doi.org/10.1007/978-3-030-97858-7 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Madenci, Erdogan Roy, Pranesh Behera, Deepak Advances in Peridynamics Mathematical Physics Engineering Mechanics Engineering Mathematics Applied Dynamical Systems Machine Learning Mathematical physics Mechanics, Applied Engineering mathematics Dynamics Nonlinear theories Machine learning |
| title | Advances in Peridynamics |
| title_auth | Advances in Peridynamics |
| title_exact_search | Advances in Peridynamics |
| title_exact_search_txtP | Advances in Peridynamics |
| title_full | Advances in Peridynamics by Erdogan Madenci, Pranesh Roy, Deepak Behera |
| title_fullStr | Advances in Peridynamics by Erdogan Madenci, Pranesh Roy, Deepak Behera |
| title_full_unstemmed | Advances in Peridynamics by Erdogan Madenci, Pranesh Roy, Deepak Behera |
| title_short | Advances in Peridynamics |
| title_sort | advances in peridynamics |
| topic | Mathematical Physics Engineering Mechanics Engineering Mathematics Applied Dynamical Systems Machine Learning Mathematical physics Mechanics, Applied Engineering mathematics Dynamics Nonlinear theories Machine learning |
| topic_facet | Mathematical Physics Engineering Mechanics Engineering Mathematics Applied Dynamical Systems Machine Learning Mathematical physics Mechanics, Applied Engineering mathematics Dynamics Nonlinear theories Machine learning |
| url | https://doi.org/10.1007/978-3-030-97858-7 |
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