Electromagnetic Simulation

Electromagnetic (EM) simulation aims to efficiently and accurately predict EM field behavior in various systems, from nanoscale devices to large-scale environments. Current research heavily utilizes machine learning, particularly generative adversarial networks (GANs) and physics-informed neural networks (PINNs), to accelerate simulations and address inverse problems, often incorporating techniques like rigorous coupled-wave analysis (RCWA) or leveraging automatic differentiation. These advancements enable faster design optimization for applications such as metasurface design, nanophotonics, and wireless communication, while also improving the accuracy and efficiency of EM modeling in diverse fields. The resulting speed and accuracy improvements are transforming the design and analysis of EM systems across numerous scientific and engineering disciplines.