School of Engineering, Brown University, Providence, RI 02912.
School of Engineering, Brown University, Providence, RI 02912;
Proc Natl Acad Sci U S A. 2021 Jun 8;118(23). doi: 10.1073/pnas.2104765118.
Data-driven approaches promise to usher in a new phase of development in fracture mechanics, but very little is currently known about how data-driven knowledge extraction and transfer can be accomplished in this field. As in many other fields, data scarcity presents a major challenge for knowledge extraction, and knowledge transfer among different fracture problems remains largely unexplored. Here, a data-driven framework for knowledge extraction with rigorous metrics for accuracy assessments is proposed and demonstrated through a nontrivial linear elastic fracture mechanics problem encountered in small-scale toughness measurements. It is shown that a tailored active learning method enables accurate knowledge extraction even in a data-limited regime. The viability of knowledge transfer is demonstrated through mining the hidden connection between the selected three-dimensional benchmark problem and a well-established auxiliary two-dimensional problem. The combination of data-driven knowledge extraction and transfer is expected to have transformative impact in this field over the coming decades.
数据驱动的方法有望开创断裂力学发展的新阶段，但目前对于如何在该领域实现数据驱动的知识提取和转移还知之甚少。与许多其他领域一样，数据稀缺是知识提取的主要挑战，不同断裂问题之间的知识转移在很大程度上仍未得到探索。在这里，提出了一种具有严格精度评估指标的数据驱动知识提取框架，并通过在小尺寸韧性测量中遇到的一个重要线性弹性断裂力学问题进行了验证。结果表明，即使在数据有限的情况下，经过精心设计的主动学习方法也可以实现准确的知识提取。通过挖掘所选三维基准问题与成熟的二维辅助问题之间的隐藏联系，证明了知识转移的可行性。在未来几十年，数据驱动的知识提取和转移的结合有望在该领域产生变革性的影响。
