Summary
Physics-Informed Neural Networks (PINNs) are an emerging class of Artificial Intelligence (AI) models that incorporate physical laws directly into their architecture, enabling fast and accurate simulations even with limited or noisy data. They show significant promise for electromagnetic (EM) simulations, particularly in managing parameter variations in real time. However, ensuring both accuracy and stability in PINN training remains a major challenge, often requiring large datasets and exhibiting sensitivity to minor input changes. To address these limitations, researchers from Stevens Instit
