DeepOTF: Learning Equations-constrained Prediction for Electromagnetic Behavior

High-quality passive devices are becoming increasingly important for the development of mobile devices and telecommunications, but obtaining such devices through simulation and analysis of electromagnetic (EM) behavior is time-consuming. To address this challenge, artificial neural network (ANN) models have emerged as an effective tool for modeling EM behavior, with NeuroTF being a representative example. However, these models are limited by the specific form of the transfer function, leading to discontinuity issues and high sensitivities. Moreover, previous methods have overlooked the physical relationship between distributed parameters, resulting in unacceptable numeric errors in the conversion results. To overcome these limitations, we propose two different neural network architectures: DeepOTF and ComplexTF. DeepOTF is a data-driven deep operator network for automatically learning feasible transfer functions for different geometric parameters. ComplexTF utilizes complex-valued neural networks to fit feasible transfer functions for different geometric parameters in the complex domain while maintaining causality and passivity. Our approach also employs an Equations-constraint Learning scheme to ensure the strict consistency of predictions and a dynamic weighting strategy to balance optimization objectives. The experimental results demonstrate that our framework shows superior performance than baseline methods, achieving up to 1700 × higher accuracy.