CAREER: Neural Network Enhanced Electromagnetics and Multiphysics Simulation Methods for RF and Microwave Reconfigurable Devices

Howard University, Washington DC

Investigators

Abstract

The rapid development of communication, sensing, and navigation systems are driving technological advances in the next-generation reconfigurable radiofrequency (RF) and microwave devices. These devices utilize tunable external stimuli such as biasing voltages or currents, electrical/magnetic/optical excitations, temperature variations, and mechanical forces to reconfigure their working properties and achieve spectrum-agile operations. To address the spatial, spectrum, and power limits, miniaturized and power-efficient RF and microwave devices are of high demand. While the reconfigurability and controllability provide unprecedented system flexibility and reliability, the design and optimization methods for such devices face great challenges coming from the structural and material complexities, multiscale design challenges, multiphysics and nonlinear interactions, and high optimization dimensionalities. This research aims at developing physics and neural network enabled electromagnetic (EM) and multiphysics simulation methods to address the challenges of multiscale, multiphysics, and nonlinear modeling for the efficient evaluation and optimization of RF and microwave reconfigurable devices. The project looks at how to develop modeling and simulation methods that utilize advanced numerical and neural network techniques for more efficient and reliable device modeling and assessments. In addition, the project has extensive education and outreach plans, as well as the development of video clips and demonstrations to disseminate the results to the public. To develop, implement, validate, and apply modeling and simulation methods for EM and multiphysics design of RF/microwave reconfigurable devices, the project will conduct four major research activities: 1) A novel all-frequency stable EM formulation and its domain-decomposition method (DDM) will be developed to address wideband and multiscale EM modeling problems. 2) A graph neural network (GNN)-aided DDM will be developed to solve large-scale EM problems with a superior efficiency. 3) Neural network (NN)-assisted multiphysics simulation method and nonlinear surrogate solvers will be investigated to address challenges in multiphysics modeling and solve nonlinear problems without the need of traditional gradient- or Newton-based iteration. 4) A physics-guided NN device optimizer will integrate the above numerical evaluation techniques to provide fast parameter sweep and shape optimization capabilities to combat the high dimensionality. The research to seamlessly integrate physics- and NN-enabled scientific computing methodologies will lead to a revolutionary simulation tool with enabling modeling and design capabilities that do not currently exist. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.