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This paper explores the performance of neural networks in materials science, discussing four classes of innovations based on neural networks. It questions the respectability of papers on neural networks and introduces empirical equations for noise modeling. The text elegantly answers whether sufficient data have been used to create models, relative to the number of coefficients. It delves into international fusion reactor reduced activation steels and discusses the exploitation of neural networks in discovering new science, explaining observations, and designing materials or processes with quantitative data expression. Unexpected outcomes and design approaches using neural networks are also highlighted.
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Materials Science Brief explanation of neural networks Four classes of innovations based on neural networks Are papers on neural networks respectable?
elegantly answers the question as to whether sufficient data have been used to create the model unreasonable to ask if sufficient data used to create model, relative to number of coefficients - may be sufficient data in some regions of input space and not in others
International Fusion Reactor Reduced activation steels
Journal of Nuclear Materials 348 (2006) 311-328 Kemp, Cottrell & Bhadeshia
Exploitation of neural networks • Discover new science • Explain observations • Design materials or processes • Quantitative expression of data
14.3% 7% tested at room temperature
14.3% 13% tested at 100 °C
7Ni 2Mn Keehan, Karlsson, Andrén, Bhadeshia, Science & Techn. Welding & Joining 11 (2006) 9-18
Exploitation of neural networks • Discover new science • Explain observations • Design materials or processes • Quantitative expression of data
9Cr1Mo Dimitriu & Bhadeshia, 2007
9Cr1Mo Dimitriu & Bhadeshia, 2007
Components of Creep Strength 2.25Cr1Mo iron + microstructure 550 °C solid solution 600 °C precipitates Murugananth & Bhadeshia, 2001
Exploitation of neural networks • Discover new science • Explain observations • Design materials, processes, • experiments • Quantitative expression of data
Suppose we fail to achieve 650°C Ferritic Creep-Resistant Steel
Exploitation of neural networks • Discover new science • Explain observations • Design materials, processes, • experiments • Quantitative expression of data
weld pool shape Mishra and DebRoy, MSE A, 454 (2007) 454
Creation of model Make predictions using model Modelling uncertainty Investigate prediction experimentally Model or data disseminated
Creation of model 1 Make predictions using model 2 Investigate prediction experimentally 2 Modelling uncertainty 2 Model or data disseminated 3