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Performance of neural networks in materials science

Title
Performance of neural networks in materials science
Date Issued
2009-04
Publisher
MANEY PUBLISHING
Abstract
Neural networks are now a prominent feature of materials science with rapid progress in all sectors of the subject. It is premature, however, to claim that the method is established. There are genuine difficulties caused by the often incomplete exploration and publication of models. The assessment presented here is an attempt to compile a loose set of guidelines for maximising the impact of any models that are created, in order to encourage thoroughness in publication to a point where the work can be independently verified.
Keywords
Neural network; Materials science; Materials modelling; Uncertainties; Errors; AUSTENITIC STAINLESS-STEEL; STRAIN-INDUCED TRANSFORMATION; PRINCIPAL COMPONENT ANALYSIS; TRIP-AIDED STEELS; MECHANICAL-PROPERTIES; MAGNETIC-PROPERTIES; RETAINED AUSTENITE; IMPACT TOUGHNESS; TENSILE-STRENGTH; TITANIUM-ALLOYS
URI
https://oasis.postech.ac.kr/handle/2014.oak/29015
DOI
10.1179/174328408X31
ISSN
0267-0836
Article Type
Article
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