Shayan Dodge is a researcher at the Department of Energy, Systems, Territory and Construction Engineering (DESTEC) at the University of Pisa.
His research focuses on computational electromagnetics and artificial intelligence for electromagnetic modeling, with particular attention to Physics-Informed Neural Networks (PINNs) and data-driven approaches for solving forward and inverse electromagnetic problems. By integrating machine learning with governing physical laws, his work aims to develop efficient and physics-consistent computational methods for complex electromagnetic systems.
His research also explores AI-based techniques for the analysis of electromagnetic phenomena, including lightning-related processes, inverse electromagnetic problems, and bioelectromagnetic applications such as transcranial magnetic stimulation (TMS).
Within this context, he contributes to research projects such as FELINES – Forecast of the Effects of Lightning in Electrical Systems, which focuses on lightning forecasting and the analysis of electromagnetic effects on electrical infrastructures, and STEM-DEEP, a project dedicated to AI-driven optimization and modeling of electromagnetic fields for biomedical applications, particularly in brain stimulation technologies.
The results of his research have been published in international scientific journals and conferences in the fields of computational electromagnetics, machine learning, and applied electromagnetics.
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