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Hardcover. Condition: Très bon. Ancien livre de bibliothèque avec équipements. Edition 1995. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 1995. Ammareal gives back up to 15% of this item's net price to charity organizations.
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Hardback or Cased Book. Condition: New. High Performance Computing and Artificial Intelligence for Geosciences. Book.
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Condition: As New. Unread book in perfect condition.
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High Performance Computing and Artificial Intelligence for Geosciences
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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High Performance Computing and Artificial Intelligence for Geosciences
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High Performance Computing and Artificial Intelligence for Geosciences
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Condition: New.
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High Performance Computing in the Geosciences: (Closed)) (Nato Science Series C:)
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High Performance Computing in the Geosciences (Nato Science Series C:, 462)
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High Performance Computing in the Geosciences
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Condition: New. pp. 284.
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Condition: New. pp. 284.
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High Performance Computing in the Geosciences
Language: English
Published by Kluwer Academic Publishers, 1995
ISBN 10: 0792334884 ISBN 13: 9780792334880
Seller: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Ireland
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Condition: New. Proceedings of the NATO Advanced Research Workshop, Centre de Physique, Les Houches, France, 21--25 June 1993 Editor(s): Dimet, Francois-Xavier Le. Series: NATO Science Series C. Num Pages: 261 pages, biography. BIC Classification: RBKC. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 244 x 170 x 17. Weight in Grams: 576. . 1995. Hardback. . . . .
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High Performance Computing in the Geosciences
Language: English
Published by Kluwer Academic Publishers, 1995
ISBN 10: 0792334884 ISBN 13: 9780792334880
Condition: New. Proceedings of the NATO Advanced Research Workshop, Centre de Physique, Les Houches, France, 21--25 June 1993 Editor(s): Dimet, Francois-Xavier Le. Series: NATO Science Series C. Num Pages: 261 pages, biography. BIC Classification: RBKC. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 244 x 170 x 17. Weight in Grams: 576. . 1995. Hardback. . . . . Books ship from the US and Ireland.
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High Performance Computing in the Geosciences
Language: English
Published by Springer Netherlands, 2012
ISBN 10: 9401040192 ISBN 13: 9789401040198
Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - High Performance Computing in the Geosciences surveys the state of the art of programs presently being developed which require high performance computing for their implementation, provides a guide for decision making in regard to computing directions in future numerical models, and provides an overview of future developments in massively parallel processing and their implications for numerical modelling in the geosciences.
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High Performance Computing in the Geosciences
Language: English
Published by Springer Netherlands, Springer Netherlands, 1995
ISBN 10: 0792334884 ISBN 13: 9780792334880
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - High Performance Computing in the Geosciences surveys the state of the art of programs presently being developed which require high performance computing for their implementation, provides a guide for decision making in regard to computing directions in future numerical models, and provides an overview of future developments in massively parallel processing and their implications for numerical modelling in the geosciences.
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High Performance Computing in the Geosciences
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Condition: As New. Unread book in perfect condition.
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HRD. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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High Performance Computing and Artificial Intelligence for Geosciences
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Add to basketHRD. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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Condition: New. Print on Demand.
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Condition: New. Print on Demand.
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Condition: New. PRINT ON DEMAND.
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Condition: new. Questo è un articolo print on demand.
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High Performance Computing in the Geosciences
Language: English
Published by Springer Netherlands, Springer Netherlands Okt 2012, 2012
ISBN 10: 9401040192 ISBN 13: 9789401040198
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -High Performance Computing in the Geosciences surveys the state of the art of programs presently being developed which require high performance computing for their implementation, provides a guide for decision making in regard to computing directions in future numerical models, and provides an overview of future developments in massively parallel processing and their implications for numerical modelling in the geosciences. 284 pp. Englisch.
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In total, this Special Issue includes 11 papers. Firstly, Qi et al. conducted research on the large-scale non-uniform parallel solution of the two-dimensional Saint-Venant equations for flood behavior modeling. Zhang et al. proposed an efficient deep learning-based mineral identification method. Subsequently, Huang et al. proposed a named entity recognition method for geological news based on BERT model. Yang et al. proposed an automatic landslide identification method to solve the problem of the time-consuming nature and low efficiency of traditional landslide identification methods. Du et al. analyzed the potential of unsupervised machine learning methods for submarine landslide prediction. Wang et al. performed parallel computations on the inversion algorithm of the two-dimensional ZTEM. Xu et al. used the sliding window method and gray relational analysis to extract features from multi-source real-time monitoring data of landslides. Furthermore, Cao et al. proposed a new method called dual encoder transform (DualET) for the short-term prediction of photovoltaic power. Hao et al. conducted a series of parallel optimizations and large-scale parallel simulations on the high-resolution ocean model. Wang et al. proposed a time series prediction model for landslide displacements using mean-based low-rank autoregressive tensor completion. Finally, Yang et al. developed a measure of site-level gross primary productivity (GPP) using the GeoMAN model.
