We propose a data-driven physics-informed neural networks(PINNs)via task-decomposition(DD-PINNs-TD)for modeling nonlinear thermal-deformation-polarization-carrier(TDPC)coupling mechanical behaviors of piezoelectric se...We propose a data-driven physics-informed neural networks(PINNs)via task-decomposition(DD-PINNs-TD)for modeling nonlinear thermal-deformation-polarization-carrier(TDPC)coupling mechanical behaviors of piezoelectric semiconductors(PSs).By embedding three-dimensional(3D),plate,and beam equations of PS structures into the constraints of the DD-PINNsTD framework,respectively,we develop three representative PINNs that exhibit significant advantages in computational efficiency and accuracy compared to traditional PINNs.Using the proposed DD-PINNs-TD models,we investigate the TDPC coupling responses of PS structures under different loadings.Numerical results demonstrate that the proposed models exhibit accuracy and stability of these models in predicting the nonlinear multi-field coupling mechanical behaviors of PSs.Notably,the plate and beam-theory-based DD-PINNs-TD models achieve superior computational efficiency relative to their 3Dequation-based counterparts.This study establishes a theoretical foundation for analyzing nonlinear multi-field coupling responses in PS stru ctures and has significant practical value in engineering applications.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12172326 and 12192210)the Natural Science Foundation of Zhejiang Province(Grant No.LZ25A020007)the National Key Research and Development Program of China(Grant No.2020YFA0711700)。
文摘We propose a data-driven physics-informed neural networks(PINNs)via task-decomposition(DD-PINNs-TD)for modeling nonlinear thermal-deformation-polarization-carrier(TDPC)coupling mechanical behaviors of piezoelectric semiconductors(PSs).By embedding three-dimensional(3D),plate,and beam equations of PS structures into the constraints of the DD-PINNsTD framework,respectively,we develop three representative PINNs that exhibit significant advantages in computational efficiency and accuracy compared to traditional PINNs.Using the proposed DD-PINNs-TD models,we investigate the TDPC coupling responses of PS structures under different loadings.Numerical results demonstrate that the proposed models exhibit accuracy and stability of these models in predicting the nonlinear multi-field coupling mechanical behaviors of PSs.Notably,the plate and beam-theory-based DD-PINNs-TD models achieve superior computational efficiency relative to their 3Dequation-based counterparts.This study establishes a theoretical foundation for analyzing nonlinear multi-field coupling responses in PS stru ctures and has significant practical value in engineering applications.
