Machine learning has found its way into almost every area of science and engineering, and we are only at the beginning of its exploration across fields. Being a popular, versatile and powerful framework, machine learning has proven most useful where classical techniques are computationally inefficient, which applies particularly to computational solid mechanics. Here, we dare to give a non-exhaustive overview of potential avenues for machine learning in the numerical modeling of solids and structures and offer our (subjective) perspective on what is yet to come.

中文翻译：

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机器学习可以为计算固体力学做什么

机器学习已经进入几乎所有科学和工程领域，而我们才刚刚开始跨领域探索。作为一种流行、通用且功能强大的框架，机器学习已被证明在经典技术计算效率低下的情况下最有用，尤其适用于计算固体力学。在这里，我们敢于对机器学习在固体和结构的数值建模中的潜在途径进行非详尽的概述，并提供我们对即将发生的事情的（主观）观点。