Shayan Dodge works at the intersection of computational electromagnetics and artificial intelligence. His research integrates high-fidelity numerical methods, including the finite element method (FEM), finite-difference time-domain (FDTD), and boundary element method (BEM), with physics-informed neural networks to develop scalable, physics-consistent surrogate models for electromagnetic systems. His work aims to advance next-generation machine learning frameworks that enable real-time electromagnetic analysis and design optimization in complex multiphysics environments.