Knowledge extraction and transfer in data-driven fracture mechanics
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- School of Engineering, Brown University, Providence, RI 02912,
- School of Engineering, Brown University, Providence, RI 02912,, School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 639798 Singapore, Singapore,, Institute of High Performance Computing, Agency for Science, Technology and Research, 138632 Singapore, Singapore
Significance Data-driven approaches have launched a new paradigm in scientific research that is bound to have an impact on all disciplines of science and engineering. However, at this juncture, the exploration of data-driven techniques in the century-old field of fracture mechanics is highly limited, and there are key challenges including accurate and intelligent knowledge extraction and transfer in a data-limited regime. Here, we propose a framework for data-driven knowledge extraction in fracture mechanics with rigorous accuracy assessment which employs active learning for optimizing data usage and for data-driven knowledge transfer that allows efficient treatment of three-dimensional fracture problems based on two-dimensional solutions.
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- SC0018113
- OSTI ID:
- 1785835
- Journal Information:
- Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 23 Vol. 118; ISSN 0027-8424
- Publisher:
- Proceedings of the National Academy of SciencesCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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