Pubblicazioni
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).
Challenges and Opportunities for the Integration of Photovoltaic Modules in Heritage Buildings Through Dynamic Building Energy Simulations
Lecture Notes in Mechanical Engineering
Photovoltaic (PV) systems are usually not recommended in heritage buildings for preserving their values and aesthetic features.
A multi-objective methodology for evaluating the investment in building-integrated hybrid renewable energy systems
The scientific literature lacks methodologies for assessing HRES investments because of complex interactions among HRES components, conflicting objectives, and uncertainty in the energy demand.