This review provides a timely exploration of several novel neural network (NN) architectures and learning methods, following a concise overview of the fundamentals of NNs and some important associated challenges. There are many benefits to using NNs, including deep learning models, in scientific research and, by understanding novel techniques better suited to certain applications, this benefit can be maximised. Finally, a few developed and emerging alternative learning paradigms are surveyed for their potential benefit to future research. The reviewed literature and accompanying discussion are of generic value well beyond steel metallurgy, and there is much to be gained from assessing methods used in other areas of materials science and further afield in order to apply them to steel metallurgy.

中文翻译：

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神经网络的进展及其在钢铁冶金中的应用潜力

在简要概述了 NN 的基础知识和一些重要的相关挑战之后，本综述及时探索了几种新型神经网络 (NN) 架构和学习方法。在科学研究中使用神经网络有很多好处，包括深度学习模型，并且通过理解更适合某些应用的新技术，可以最大限度地提高这种好处。最后，调查了一些已发展和新兴的替代学习范式，以了解它们对未来研究的潜在益处。所审查的文献和随附的讨论具有远远超出钢铁冶金的一般价值，并且可以通过评估材料科学其他领域和更远领域中使用的方法将其应用于钢铁冶金中获得很多。