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• W4224285428 startingPage "107282" @default.
• W4224285428 abstract "Only a few studies have focused on the simulation of the crack propagation of quasi-brittle materials under complex loading inspired by physical-informed neural networks (PINN). Guided by the energy minimization principle rather than labelled data, we reconstruct the solution of displacement field after damage to predict crack propagation using PINN which maintains the thermodynamics consistency inherited from our proposed variable four-parameter damage model. Additionally, the framework of incremental pattern performs relatively efficiently with transfer learning. Consequently, a novel method for better convergence based on domain decomposition theory is proposed to identify complex boundaries. Based on gradient pathology, we develop a finite basis algorithm to solve the ill-condition problem. Whether under uniaxial tension, pure shear or mixed mode loading, the prediction results of displacement field fit well with simulations in the literature. Our research is meaningful for improving the generalization of neural networks and accelerating the optimization process which are necessary for further engineering applications." @default.
• W4224285428 created "2022-04-26" @default.
• W4224285428 creator A5002096737 @default.
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• W4224285428 date "2022-06-01" @default.
• W4224285428 modified "2023-10-15" @default.
• W4224285428 title "Physics-informed machine learning model for computational fracture of quasi-brittle materials without labelled data" @default.
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• W4224285428 doi "https://doi.org/10.1016/j.ijmecsci.2022.107282" @default.
• W4224285428 hasPublicationYear "2022" @default.
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