Knowledge extraction and transfer in data-driven fracture mechanics.
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.
['Liu|Xing|X|0000-0002-4637-0109', 'Athanasiou|Christos E|CE|0000-0002-0812-4062', 'Padture|Nitin P|NP|0000-0001-6622-8559', 'Sheldon|Brian W|BW|0000-0002-9593-891X', 'Gao|Huajian|H|0000-0002-8656-846X']
fracture mechanics fracture toughness machine learning transfer learning