Performance of neural networks in materials science

被引：98

作者：

Bhadeshia, H. K. D. H. ^{[1
,2
]}

Dimitriu, R. C. ^{[1
]}

Forsik, S. ^{[1
]}

Pak, J. H. ^{[2
]}

Ryu, J. H. ^{[2
]}

机构：

[1] Univ Cambridge, Cambridge CB2 3QZ, England

[2] Pohang Univ Sci & Technol, Grad Inst Ferrous Technol, Pohang 790784, South Korea

来源：

MATERIALS SCIENCE AND TECHNOLOGY | 2009年 / 25卷 / 04期

基金：

英国工程与自然科学研究理事会;

关键词：

Neural network; Materials science; Materials modelling; Uncertainties; Errors; AUSTENITIC STAINLESS-STEEL; STRAIN-INDUCED TRANSFORMATION; PRINCIPAL COMPONENT ANALYSIS; MECHANICAL-PROPERTIES; MAGNETIC-PROPERTIES; RETAINED AUSTENITE; IMPACT TOUGHNESS; PART; PREDICTION; STRENGTH;

D O I：

10.1179/174328408X311053

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：504 / 510

页数：7

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