Piezoelectric semiconductors(PSs)have widespread applications in semiconductor devices due to the coexistence of piezoelec-tricity and semiconducting properties.It is very important to conduct a theoretical analysis o...Piezoelectric semiconductors(PSs)have widespread applications in semiconductor devices due to the coexistence of piezoelec-tricity and semiconducting properties.It is very important to conduct a theoretical analysis of PS structures.However,the present of nonlinearity in the partial differential equations(PDEs)that describe those multi-feld coupling mechanical behaviors of PSs poses a significant mathematical challenge when studying these PS structures.In this paper,we present a novel approach based on machine learning for solving multi-field coupling problems in PS structures.A physics-informed neural networks(PINNs)is constructed for predicting the multi-field coupling behaviors of PS rods with extensional deforma-tion.By utilizing the proposed PINNs,we evaluate the multi-field coupling responses of a ZnO rod under static and dynamic axial forces.Numerical results demonstrate that the proposed PINNs exhibit high accuracy in solving both static and dynamic problems associated with Ps structures.It provides an effective approach to predicting the nonlinear multi-feld coupling phe-nomena in PS structures.展开更多
基金supported by the National Natural Science Foundation of China[11972139]Natural Science Foundation of Zhejiang Province[LR21A020002]Specialized research projects of Huanjiang Laboratory。
文摘Piezoelectric semiconductors(PSs)have widespread applications in semiconductor devices due to the coexistence of piezoelec-tricity and semiconducting properties.It is very important to conduct a theoretical analysis of PS structures.However,the present of nonlinearity in the partial differential equations(PDEs)that describe those multi-feld coupling mechanical behaviors of PSs poses a significant mathematical challenge when studying these PS structures.In this paper,we present a novel approach based on machine learning for solving multi-field coupling problems in PS structures.A physics-informed neural networks(PINNs)is constructed for predicting the multi-field coupling behaviors of PS rods with extensional deforma-tion.By utilizing the proposed PINNs,we evaluate the multi-field coupling responses of a ZnO rod under static and dynamic axial forces.Numerical results demonstrate that the proposed PINNs exhibit high accuracy in solving both static and dynamic problems associated with Ps structures.It provides an effective approach to predicting the nonlinear multi-feld coupling phe-nomena in PS structures.
