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Machine learning methods for data driven theory in the physical sciences with applications to confinement regime identification in nuclear fusion

Publication Data

Title Machine learning methods for data driven theory in the physical sciences with applications to confinement regime identification in nuclear fusion
Contributors A. Murari, European Fusion Development Agreement
Publisher EFDA, Culham Science Centre
Publication date 2009
Publication location Abingdon
Language English

Descriptive Notes

"EFDA-JET-PR(09)04"--Cover.
"Preprint of paper to be submitted for publication in Reports on progress in physics".

Topics

Machine learning
Nuclear fusion
Nuclear fusion :: Data processing
Tokamaks
England

Catalogue Data

OBNB ID GBB072021
ISBN 10 n/a
ISBN 13 n/a
Type BibliographicResource, Book

ISBD

P1053 28 p.
P1042 Includes bibliographical references (p. 20-21).

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Machine learning methods for data driven theory in the physical sciences with applications to confinement regime identification in nuclear fusion. Published by EFDA, Culham Science Centre in 2009. Publication and catalogue information, links to buy online and reader comments.