Further investigation of convolutional neural networks applied in computational electromagnetism under physics‐informed consideration

Abstract

Convolutional neural networks (CNN) have shown great potentials and have been proven to be an effective tool for some image‐based deep learning tasks in the field of computational electromagnetism (CEM). In this work, an energy‐based physics‐informed neural network (EPINN) is proposed for low‐frequency electromagnetic computation. Two different physics‐informed loss functions are designed. To help the network focus on the region of interest instead of computing the whole domain on average, the magnetic energy norm error loss function is proposed. Besides, the methodology of energy minimization is integrated into the CNN by introducing the magnetic energy error loss function. It is observed that the introduction of the physics‐informed loss functions improved the accuracy of the network with the same architecture and database. Meanwhile, these changes also cause the network to be more sensitive to some hyperparameters and makes the training process oscillate or even diverge. To address this issue, the sensitivity of the network hyperparameters for both physics‐informed loss functions are further investigated. Numerical experiments demonstrate that the proposed approaches have good accuracy and efficiency with fine‐tuned hyperparameters. Furthermore, the post‐test illustrates that the EPINN has excellent interpolation performance and can obtain good extrapolation results under certain restrictions.

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

“…Then, the regularization of non-physical solutions is performed as composite loss functions. Physics-informed neural network (PINN) (Raissi et al, 2019;Gong and Tang, 2022) is one the most representative coupled physics-DL solutions. Recently, Liu et al (2022) embeds the forward operator into the network architecture.…”

Multi-Scale Feature Fusion Convolutional Neural Networks for Fault Diagnosis of Electromechanical Actuator

“…Unlike traditional methods, deep learning can directly learn the effective fault features adaptively from monitoring signals and perform the fault classification at the same time, thereby achieving end-to-end fault diagnosis. Particularly, the CNN has achieved superior performance in various fault-diagnosis tasks [14][15][16] due to its features of weight sharing, local connection, and multiple convolution kernels. The superior performance of one-dimensional CNN (1DCNN) in the EMA fault-diagnosis problem compared to traditional data-based methods has been demonstrated in prior work [17].…”