[1] Physics-Informed Deep Operator Network for 3-D Time-Domain Electromagnetic Modeling

We develop a physics-informed deep operator network (PI-DON) for solving realistic 3-D electromagnetic problems. The training process of PI-DON is unsupervised, eliminating the need to generate ground-truth data and thereby improving efficiency compared to traditional deep neural networks. As an electromagnetic solver, PI-DON demonstrates competitive efficiency compared to finite-difference time-domain (FDTD) for a single run, even when accounting for training time. After training, PI-DON demonstrates strong generalizability, enabling accurate and efficient modeling of cases with geometric and material variations, making it well-suited for uncertainty analysis and design optimization. We show the high accuracy, efficiency, and robust generalizability of the PI-DON solver through the modeling and uncertainty analysis of realistic 3-D electromagnetic problems.

[2] Hybrid Physics-Informed Neural Network for the Wave Equation with Unconditionally Stable Time-Stepping

We introduce a novel physics-informed approach for neural network-based three-dimensional electromagnetic modeling. The proposed method combines standard leap-frog time-stepping with neural network-driven automatic differentiation for spatial derivative calculations in the wave equation. This methodology effectively addresses the challenge of accurately modeling high-frequency electromagnetic fields with physics-informed neural networks, often characterized as ``spectral bias”, in the time domain. We demonstrate that the resultant numerical scheme enables unconstrained time-stepping with respect to stability, in contrast to the Finite-Difference Time-Domain method, which is subject to the Courant stability limit. Furthermore, the use of neural networks allows for seamless GPU acceleration. We rigorously evaluate the accuracy and efficiency of this finite-difference automatic differentiation approach, by comprehensive numerical experiments.

[3] Coupled Electromagnetic-Thermal Analysis for Temperature-Dependent Materials with Physics-Informed Neural Networks

We present a Physics-Informed Neural Network (PINN) based method for the coupled electromagnetic-thermal analysis of microwave structures with temperature-dependent materials. Combined with the Finite-Difference-Time domain technique, the proposed approach efficiently handles the dynamic change of material parameters with temperature, without compromising accuracy. We demonstrate this method with the electromagnetic-thermal modeling of a micro-electro-mechanical switch on a coplanar waveguide. This study demonstrates the potential of employing PINNs in real-world multiphysics applications for the first time.

[4] Electromagnetic-Thermal Analysis With FDTD and Physics-Informed Neural Networks

We present the coupling of the finite-difference time-domain (FDTD) method for electromagnetic field simulation, with a physics-informed neural network based solver for the heat equation. To this end, we employ a physics-informed U-Net instead of a numerical method to solve the heat equation. This approach enables the solution of general multiphysics problems with a single-physics numerical solver coupled with a neural network, overcoming the questions of accuracy and efficiency that are associated with interfacing multiphysics equations. By embedding the heat equation and its boundary conditions in the U-Net, we implement an unsupervised training methodology, which does not require the generation of ground-truth data. We test the proposed method with general 2-D coupled electromagnetic-thermal problems, demonstrating its accuracy and efficiency compared to standard finite-difference based alternatives.

[5] Deep Neural Networks for Rapid Simulation of Planar Microwave Circuits Based on their Layouts

We propose a deep learning based methodology for the rapid simulation of planar microwave circuits based on their layouts. We train convolutional neural networks to compute the scattering parameters of general, two-port circuits consisting of a metallization layer printed on a grounded dielectric substrate, by processing the metallization pattern along with the thickness and dielectric permittivity of the substrate. This approach harnesses the efficiency of convolutional neural networks with pattern recognition tasks and extends previous efforts to employ neural networks for the simulation of parameterized circuit geometries. Training is based on full-wave simulation data generated via the Finite-Difference Time-Domain (FDTD) method over a target frequency range. To accelerate the generation of such data, we build a hybrid neural network including recurrent neural network modules, to compensate numerical dispersion errors in coarse-grid FDTD. This novel dispersion compensation scheme allows us to generate accurate FDTD training data from fast, coarse-grid simulations.

[6] 2D Electromagnetic Solver Based on Deep Learning Technique

Although the deep learning technique has been introduced into computational physics in recent years, the feasibility of applying it to solve electromagnetic (EM) scattering field from arbitrary scatters remains open. In this article, the convolutional neural network (CNN) has been employed to predict the EM field scattered by complex geometries under plane-wave illumination. The 2-D finite-difference frequency-domain (FDFD) algorithm, wrapped by a module to randomly generate complex scatters from basic geometries, is employed to produce training data for the network. The multichannel end-to-end CNN is modified and combined with residual architecture and skip connection, which can speed up convergence and optimize network performance, to form the EM-net. The well-trained EM-net has good performance in this problem since it is compatible with different shapes, multiple kinds of materials, and different propagation directions of the incident waves. The effectiveness of the proposed EM-net has been validated by numerical experiments, and the average numerical error can be as small as 1.23%. Meanwhile, its speedup ratio over the FDFD method is as large as 2000.

[7] Fabriccio: Touchless Gestural Input on Interactive Fabrics

We present Fabriccio, a touchless gesture sensing technique developed for interactive fabrics using Doppler motion sensing. Our prototype was developed using a pair of loop antennas (one for transmitting and the other for receiving), made of conductive thread that was sewn onto a fabric substrate. The antenna type, configuration, transmission lines, and operating frequency were carefully chosen to balance the complexity of the fabrication process and the sensitivity of our system for touchless hand gestures, performed at a 10 cm distance. Through a ten-participant study, we evaluated the performance of our proposed sensing technique across 11 touchless gestures as well as 1 touch gesture. The study result yielded a 92.8% cross-validation accuracy and 85.2% leave-one-session-out accuracy. We conclude by presenting several applications to demonstrate the unique interactions enabled by our technique on soft objects.

[8] ADS-B Message Authentication Using Features of Signal in Transition Regions

Automatic Dependent Surveillance - Broadcast (ADS-B) is an essential communication protocol used in modern air traffic control. However, lacking security measures such as authentication and encryption, ADS-B messages can be forged and modified easily by malicious attackers. This paper addresses the problem of authenticating an ADS-B message by means of specific emitter identification (SEI), employing the unintentional modulation on pulse (UMOP). A complete identification system is presented, including data acquisition, feature extraction and classification. In order to create the feature vector for classification, the transition region in a pulse is first delimited and then used for extraction of UMOP features through Hilbert-Huang Transform. The performance of the method is tested by real signal from 30 ADS-B transmitters. Our proposed method is shown to achieve an recognition ratio of over 94%, and performs better under low SNR compared with two previous techniques. It is demonstrated that the SEI technique proposed can be used as an additional tool to enhance the security of ADS-B protocol.