About Me
I am a PhD researcher in Electrical Engineering at the University of Pisa, Italy, working at the intersection of Scientific Machine Learning, Computational Electromagnetics, and AI for Physics.
My research focuses on applying AI to electromagnetic simulation, power and energy systems, bioelectromagnetics, inverse problems, and scientific computing.
I develop Physics-Informed Neural Networks (PINNs), variational PINNs, neural operators, generative AI models, and deep learning frameworks for solving PDEs, surrogate modeling, signal analysis, optimization, and simulation acceleration.
Research Areas
Scientific Machine Learning for PDEs
PINNs, variational methods, neural operators, and physics-informed learning.
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AI for Power and Energy Systems
Lightning localization, peak-current estimation, and power-system protection.
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AI for Biomedical Electromagnetics
Generative models for patient-specific TMS, MRI-based planning, and coil positioning.
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Geophysical Inversion and Scientific Computing
AI-based geophysical inversion, electrical resistivity tomography, and conductivity mapping.
Explore Research →Key Contributions
INI-VPINN
A variational PINN framework with implicit treatment of Neumann and interface conditions for multi-material domains with complex geometries.
STAR-PINN
A STacked Adaptive Residual PINN approach for time-domain magnetic diffusion in nonlinear materials.
Hybrid BEM–PINN
A hybrid Boundary Element–Physics-Informed Neural Network method for electromagnetic analysis.
Academic Activities
Recent News
[08/2026] Our work on INI-VPINN has been published in the Journal of Computational Physics (JCP). [arXiv] [Elsevier]
[06/2026] Participated in ACES 2026 and CEFC 2026 in Thessaloniki, Greece, contributing to five works spanning Scientific Machine Learning, Physics-Informed Neural Networks, Computational Electromagnetics and Biomedical Engineering.
[05/05/2026] Joined the IEEE Journal on Multiscale and Multiphysics Computational Techniques (JMMCT) as a Student Editorial Assistant. Excited to contribute to the journal and further develop my research and editorial experience. [IEEE JMMCE]
[14/04/2026] Our paper, “PINN-based resolution of inverse non-linear magnetostatic problems,” has been published in COMPEL.