A Preliminary Study on the Resolution of Electro-Thermal Multi-Physics Coupling Problem Using Physics-Informed Neural Network (PINN)

作者：Yaoyao Ma, Xiaoyu Xu, Shuai Yan, Zhuoxiang Ren · 发表于：Algorithms · 年份：2022 · DOI：10.3390/a15020053 · 被引用次数：33 · 研究领域：Model Reduction and Neural Networks、Electrostatic Discharge in Electronics、Probabilistic and Robust Engineering Design

The problem of electro-thermal coupling is widely present in the integrated circuit (IC). The accuracy and efficiency of traditional solution methods, such as the finite element method (FEM), are tightly related to the quality and density of mesh construction. Recently, PINN (physics-informed neural network) was proposed as a method for solving differential equations. This method is mesh free and generalizes the process of solving PDEs regardless of the equations’ structure. Therefore, an experiment is conducted to explore the feasibility of PINN in solving electro-thermal coupling problems, which include the electrokinetic field and steady-state thermal field. We utilize two neural networks in the form of sequential training to approximate the electric field and the thermal field, respectively. The experimental results show that PINN provides good accuracy in solving electro-thermal coupling problems.