5 papers
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…
Predictability and Fairness in Load Aggregation with Deadband
F. V. Difonzo, M. Roubalik, J. Marecek
Virtual power plants and load aggregation are becoming increasingly common. There, one regulates the aggregate power output of an ensemble of distributed energy resources (DERs). M…
On the Injectivity of Mean Value Mapping between Convex Quadrilaterals
Luca Dieci, Fabio V. Difonzo
We prove that Mean Value mapping between convex quadrilaterals is injective, affirmatively proving a conjecture stated in M. S. Floater and J. Kosinka, On the injectivity of Wachsp…
Stochastic Langevin Differential Inclusions with Applications to Machine Learning
Fabio V. Difonzo, Vyacheslav Kungurtsev, Jakub Marecek
Stochastic differential equations of Langevin-diffusion form have received significant attention, thanks to their foundational role in both Bayesian sampling algorithms and optimiz…