Large-Scale Structure of the Universe: Cosmological Simulations and Machine Learning
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
Springer Nature Singapore
2022
Singapore Springer |
| Ausgabe: | 1st ed. 2022 |
| Schriftenreihe: | Springer Theses, Recognizing Outstanding Ph.D. Research
|
| Schlagworte: | |
| Online-Zugang: | BFB01 FKE01 TUM01 UBM01 UBT01 UBY01 Volltext |
| Beschreibung: | 1 Online-Ressource (XII, 120 p. 46 illus., 44 illus. in color) |
| ISBN: | 9789811958809 |
| ISSN: | 2190-5061 |
| DOI: | 10.1007/978-981-19-5880-9 |
Internformat
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Datensatz im Suchindex
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| author | Moriwaki, Kana |
| author_facet | Moriwaki, Kana |
| author_role | aut |
| author_sort | Moriwaki, Kana |
| author_variant | k m km |
| building | Verbundindex |
| bvnumber | BV048637908 |
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| dewey-full | 523.1 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 523 - Specific celestial bodies and phenomena |
| dewey-raw | 523.1 |
| dewey-search | 523.1 |
| dewey-sort | 3523.1 |
| dewey-tens | 520 - Astronomy and allied sciences |
| discipline | Physik |
| discipline_str_mv | Physik |
| doi_str_mv | 10.1007/978-981-19-5880-9 |
| edition | 1st ed. 2022 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:17:05Z |
| indexdate | 2024-07-10T09:44:43Z |
| institution | BVB |
| isbn | 9789811958809 |
| issn | 2190-5061 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034012873 |
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| physical | 1 Online-Ressource (XII, 120 p. 46 illus., 44 illus. in color) |
| psigel | ZDB-2-PHA ZDB-2-PHA_2022 |
| publishDate | 2022 |
| publishDateSearch | 2022 |
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| publisher | Springer Nature Singapore Springer |
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| series2 | Springer Theses, Recognizing Outstanding Ph.D. Research |
| spelling | Moriwaki, Kana Verfasser aut Large-Scale Structure of the Universe Cosmological Simulations and Machine Learning by Kana Moriwaki 1st ed. 2022 Singapore Springer Nature Singapore 2022 Singapore Springer 1 Online-Ressource (XII, 120 p. 46 illus., 44 illus. in color) txt rdacontent c rdamedia cr rdacarrier Springer Theses, Recognizing Outstanding Ph.D. Research 2190-5061 Cosmology Machine Learning Astrophysics Astronomy, Observations and Techniques Machine learning Astronomy—Observations Erscheint auch als Druck-Ausgabe 978-981-1958-79-3 Erscheint auch als Druck-Ausgabe 978-981-1958-81-6 Erscheint auch als Druck-Ausgabe 978-981-1958-82-3 https://doi.org/10.1007/978-981-19-5880-9 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Moriwaki, Kana Large-Scale Structure of the Universe Cosmological Simulations and Machine Learning Cosmology Machine Learning Astrophysics Astronomy, Observations and Techniques Machine learning Astronomy—Observations |
| title | Large-Scale Structure of the Universe Cosmological Simulations and Machine Learning |
| title_auth | Large-Scale Structure of the Universe Cosmological Simulations and Machine Learning |
| title_exact_search | Large-Scale Structure of the Universe Cosmological Simulations and Machine Learning |
| title_exact_search_txtP | Large-Scale Structure of the Universe Cosmological Simulations and Machine Learning |
| title_full | Large-Scale Structure of the Universe Cosmological Simulations and Machine Learning by Kana Moriwaki |
| title_fullStr | Large-Scale Structure of the Universe Cosmological Simulations and Machine Learning by Kana Moriwaki |
| title_full_unstemmed | Large-Scale Structure of the Universe Cosmological Simulations and Machine Learning by Kana Moriwaki |
| title_short | Large-Scale Structure of the Universe |
| title_sort | large scale structure of the universe cosmological simulations and machine learning |
| title_sub | Cosmological Simulations and Machine Learning |
| topic | Cosmology Machine Learning Astrophysics Astronomy, Observations and Techniques Machine learning Astronomy—Observations |
| topic_facet | Cosmology Machine Learning Astrophysics Astronomy, Observations and Techniques Machine learning Astronomy—Observations |
| url | https://doi.org/10.1007/978-981-19-5880-9 |
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