Machine Learning on Phase-Field Models of Sea Ice Fracture
H. Dinh, D. Giannakis, G. Stadler, and J. Slawinska
New York University
Fractures of individual sea ice ﬂoes inﬂuence large scale parameters such as ﬂoe size distribution and basin scale rheology. The complexity of fracture patterns sharply changes as material shape, stiﬀness and external forcings vary. We have implemented a phase-ﬁeld model of brittle fractures, on ice ﬂoes, that simulates complex fracture behavior in response to physical parameters. Additionally, we are developing a machine learn ing framework to detect and predict changes in the fracture patterns generated by any model of fracture.
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