Data Driven Prognosis of Fracture Dynamics Using Tensor Train and Gaussian Process Regression

Data Driven Prognosis of Fracture Dynamics Using Tensor Train and Gaussian Process Regression

Accurate prediction of the mechanical failure of structural components plays an important role in the design of engineering structures. However, the fracture process is challenging to model numerically due to the existence of an elastic singularity at the crack tip. The phase field model (PFM) is on...

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Bibliographic Details
Main Authors: Pham Luu Trung Duong, Shaista Hussain, Mark Hyunpong Jhon, Nagarajan Raghavan
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Data driven
crack growth
fracture dynamics
Gaussian process regression
reduced order model
phase field
Online Access:https://ieeexplore.ieee.org/document/9284488/

Internet

https://ieeexplore.ieee.org/document/9284488/

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