Pubblicazioni
A deep learning based lightning location system
Electric Power Systems Research
The paper presents a new approach for lightning location and peak current estimation based on Deep Learning (DL) algorithms.
A Deep Learning Based Prediction of Specific Absorption Rate Hot‐Spots Induced by Broadband Electromagnetic Devices
The rapid growth of wearable electromagnetic devices has raised concerns about the potential health effects of electromagnetic fields, particularly due to their interaction with biological tissues.
A Novel Hybrid Boundary Element—Physics Informed Neural Network Method for Numerical Solutions in Electromagnetics
In this contribution the authors propose a hybrid Boundary Element Method – Physics Informed Neural Networks (BEM – PINN) approach, to be used for the resolution of partial differential equations arising when formulating boundary-value problems in electromagnetism.
A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials
This work explores the use of Physics-Informed Neural Networks (PINNs) and a newly proposed approach, called the STacked Adaptive Residual PINN (STAR-PINN), to solve magnetic diffusion problems in the magneto quasi static regime. The study covers both one- and two-dimensional domains.
Cost-benefit analysis of hybrid photovoltaic/thermal collectors in a nearly zero-energy building
Energies
This paper analyzes the use of hybrid photovoltaic/thermal (PVT) collectors in nearly zero-energy buildings (NZEBs).