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Innovations in Neural Networks for Materials Science: A Critique

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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Innovations in Neural Networks for Materials Science: A Critique

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  1. Performance of Neural Networks

  2. Materials Science Brief explanation of neural networks Four classes of innovations based on neural networks Are papers on neural networks respectable?

  3. Empirical Equations

  4. noise

  5. modelling uncertainty

  6. 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

  7. International Fusion Reactor Reduced activation steels

  8. Journal of Nuclear Materials 348 (2006) 311-328 Kemp, Cottrell & Bhadeshia

  9. Exploitation of neural networks • Discover new science • Explain observations • Design materials or processes • Quantitative expression of data

  10. Neural networks: unexpected outcomes

  11. 14.3%  7%  tested at room temperature

  12. 14.3%  13%  tested at 100 °C

  13. Neural networks: design

  14. 7Ni 2Mn Keehan, Karlsson, Andrén, Bhadeshia, Science & Techn. Welding & Joining 11 (2006) 9-18

  15. Exploitation of neural networks • Discover new science • Explain observations • Design materials or processes • Quantitative expression of data

  16. 9Cr1Mo Dimitriu & Bhadeshia, 2007

  17. 9Cr1Mo Dimitriu & Bhadeshia, 2007

  18. Components of Creep Strength 2.25Cr1Mo iron + microstructure 550 °C solid solution 600 °C precipitates Murugananth & Bhadeshia, 2001

  19. Exploitation of neural networks • Discover new science • Explain observations • Design materials, processes, • experiments • Quantitative expression of data

  20. Suppose we fail to achieve 650°C Ferritic Creep-Resistant Steel

  21. Exploitation of neural networks • Discover new science • Explain observations • Design materials, processes, • experiments • Quantitative expression of data

  22. weld pool shape Mishra and DebRoy, MSE A, 454 (2007) 454

  23. Five steps in the creation of meaningful neural networks

  24. Creation of model Make predictions using model Modelling uncertainty Investigate prediction experimentally Model or data disseminated

  25. Creation of model 1 Make predictions using model 2 Investigate prediction experimentally 2 Modelling uncertainty 2 Model or data disseminated 3

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