2 papers
math.NA2025
Physics Informed Neural Networks for Learning the Horizon Size in Bond-Based Peridynamic Models
Fabio V. Difonzo, Luciano Lopez, Sabrina F. Pellegrino
This paper broaches the peridynamic inverse problem of determining the horizon size of the kernel function in a one-dimensional model of a linear microelastic material. We explore…
math.NA2024
Inverse Physics-Informed Neural Networks for transport models in porous materials
Marco Berardi, Fabio Difonzo, Matteo Icardi
Physics-Informed Neural Networks (PINN) are a machine learning tool that can be used to solve direct and inverse problems related to models described by Partial Differential Equati…