Abstract:

The Physical-informed Neural Network(PINN) is a new type of deep learning model,but it cannot effectively solve the problem that high-order nonlinear equations are difficult to solve in circuit DC analysis.To address this problem,this paper proposes a novel and PINN-based learning simulation model to achieve an efficient simulation analysis and accurate solutions of DC operating points in nonlinear circuits.Specifically,the nonlinear device IV characteristic equation and modified node analysis(MNA) equation are simultaneously exploited as a regularization term for the loss function,and the node admittance matrix and independent power supply values are directly substituted into the PINN as prior knowledge for training to obtain the final DC operating point learning simulation model,thereby effectively predicting the node voltage value and completing the nonlinear solution of different device models.To validate the proposed PINN learning model,we conduct experiments on three typical nonlinear devices.The simulation results show that the maximum relative error of the proposed PINN learning model is less than 4.30% compared with the theoretical values,thus effectively solving the problem that the traditional numerical algorithms converge with difficulty when solving the DC operating points in nonlinear circuits.As compared with Gmin and source-stepping methods,the average prediction accuracy of the proposed PINN model increases by 0.11% and 0.23%,respectively.This illustrates that our method has a higher learning efficiency and a good stability while requiring fewer samples.

Key words: physics-informed neural network, nonlinear circuits, DC analysis, prediction model