I work at the intersection of physics and artificial intelligence, developing advanced computational frameworks that combine high-fidelity numerical solvers with modern machine learning.
⚡ Bridging first-principles physics and artificial intelligence for next-generation electromagnetic systems.
⚙️ AI for Electromagnetics: PINNs · Neural Operators · Inverse Modeling
📡 Computational Electromagnetics: FEM · FDTD · BEM · Multiphysics
⚡ Lightning & Power Systems: Localization · Induced Overvoltages
🧬 Bioelectromagnetics: TMS · SAR · Hyperthermia
🚀 High-Performance Computing: CUDA · GPU Acceleration · Parallel Solvers
💻 Languages: Python · MATLAB · C++
🤖 Machine Learning: TensorFlow · PyTorch · JAX
📊 Scientific Computing: NumPy · SciPy · CUDA
🧲 EM Software: ANSYS Maxwell · CST Studio · SimNIBS · Onelab
🐧 Environment: Linux · Git
Design of physics-consistent neural architectures (PINNs) for forward and inverse electromagnetic field problems, including nonlinear and time-domain systems.
🔗 Best Paper: A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion, IEEE Access, 2025, Link
Integration of classical numerical solvers (BEM, FEM) with neural networks to accelerate and stabilize electromagnetic simulations.
🔗 Best Paper: A Novel Hybrid Boundary Element–Physics Informed Neural Network Method, IEEE Access, 2024, PaperGitHub
Development of deep learning frameworks for lightning location, peak current estimation, and induced overvoltage prediction in electrical networks.
🔗 Best Paper: A Deep Learning Based Lightning Location System, Electric Power Systems Research, 2025, Link
Computational modeling of electromagnetic interactions in biological systems, including transcranial magnetic stimulation (TMS) and SAR prediction in biomedical applications.
🔗 Best Paper: A Deep Learning Based Prediction of Specific Absorption Rate Hot-Spots, IET Science, Measurement & Technology, 2025, Link
GPU-accelerated FDTD solvers for electromagnetic wave propagation and plasma modeling.
🔗 Best Paper: Application of GPU-Accelerated FDTD Method to Electromagnetic Wave Propagation in Plasma, Link
📧 Email: dodgeshayan@gmail.com, shayan.dodge@ing.unipi.it
🆔 ORCID: 0000-0002-8323-2290
🔗 Linkedin: linkedin.com/in/shayan-dodge-441453204
🎓 Google Scholar: Google Scholar/Shayan Dodge
Fast model order reduction based approach for transcranial magnetic stimulation with varying coil positioning
IEEE Access, 2026.
DOI: 10.1109/ACCESS.2026.3668478A Comparison of Machine Learning and Classical Numerical Approaches for the Resolution of Electromagnetics Problems
IET Science, Measurement & Technology, 2025.
DOI: https://doi.org/10.1049/smt2.70034Weak Formulation for Physics-Informed Neural Networks in the Resolution of Analysis Problems in Electromagnetics
IEEE Transactions on Magnetics, 2025.
DOI: 10.1109/TMAG.2025.3626152Unilateral EMG-Guided Botulinum Toxin for Retrograde Cricopharyngeus Dysfunction
Toxins, 2025.
DOI: https://doi.org/10.3390/toxins17090458Relating Transmission Line Overvoltages and Lightning Location: A Machine Learning–Based Procedure
COMPEL, 2025.
DOI: https://doi.org/10.1108/COMPEL-12-2024-0521A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials
IEEE Access, 2025.
DOI: 10.1109/ACCESS.2025.3597869A Deep Learning Based Prediction of Specific Absorption Rate Hot-Spots Induced by Broadband Electromagnetic Devices
IET Science, Measurement & Technology, 2025.
DOI: https://doi.org/10.1049/smt2.70009A Deep Learning Based Lightning Location System
Electric Power Systems Research, 2025.
https://doi.org/10.1016/j.epsr.2025.111437Preliminary Breakdown Pulses (PBP): A Review on Available Data and Models
Electric Power Systems Research, 2025.
DOI: https://doi.org/10.1016/j.epsr.2025.111463A Novel Hybrid Boundary Element–Physics Informed Neural Network Method for Numerical Solutions in Electromagnetics
IEEE Access, 2024.
DOI: 10.1109/ACCESS.2024.3500039Characterization of Microwave Heating for Hyperthermia Cancer Treatment
Waves in Random and Complex Media, 2024.
DOI: https://doi.org/10.1080/17455030.2021.1905911Application of GPU-Accelerated FDTD Method to Electromagnetic Wave Propagation in Plasma
arXiv, 2022.
https://arxiv.org/abs/2211.05647