9 citations · 14 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
A Randomized Runge-Kutta Method for time-irregular delay differential equations
Fabio V. Difonzo, Paweł Przybyłowicz, Yue Wu +1
In this paper we investigate the existence, uniqueness and approximation of solutions of delay differential equations (DDEs) with the right-hand side functions that ar…
Convergence Analysis of a Spectral Numerical Method for a Peridynamic Formulation of Richards' Equation
Fabio V. Difonzo, Sabrina F. Pellegrino
We study the implementation of a Chebyshev spectral method with forward Euler integrator to investigate a peridynamic nonlocal formulation of Richards' equation. We prove the conve…
Physics Informed Neural Networks for an Inverse Problem in Peridynamic Models
Fabio Vito Difonzo, Luciano Lopez, Sabrina Francesca Pellegrino
Deep learning is a powerful tool for solving data driven differential problems and has come out to have successful applications in solving direct and inverse problems described by…
Existence, uniqueness and approximation of solutions to Carathéodory delay differential equations
Fabio V. Difonzo, Paweł Przybyłowicz, Yue Wu
In this paper we address the existence, uniqueness and approximation of solutions of delay differential equations (DDEs) with Carathéodory type right-hand side functions. We provid…