Author:
Nash Will,Drummond Tom,Birbilis Nick
Publisher
Springer Science and Business Media LLC
Subject
Materials Chemistry,Materials Science (miscellaneous),Chemistry (miscellaneous),Ceramics and Composites
Reference59 articles.
1. Gin, S., Dillmann, P. & Birbilis, N. Material degradation foreseen in the very long term: The case of glasses and ferrous metals. npj Mater. Degrad. 1, 10 (2017).
2. Schmidhuber, J. Deep Learning in neural networks: An overview. Neural Netw. 61, 85–117 (2015).
3. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet Classification with Deep Convolutional Neural Networks. in Proceedings of the 25th International Conference on Neural Information Processing Systems 1–9 (IEEE, New Jersey, 2012).
4. Dimiduk, D. M., Holm, E. A. & Niezgoda, S. R. Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integr. Mater. Manuf. Innov. 7, 157–172 (2018).
5. Zhang, Y. et al. Using machine learning for scientific discovery in electronic quantum matter visualization experiments. arXiv pre-print at
http://arxiv.org/abs/1808.00479
(2018).
Cited by
114 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献