activity
20152026
most citedVariational Physics Informed Neural Networks: the role of quadratures and test functions

75 citations · 369 across the 32 of their papers we have counts for

collaborators

35 papers

math.NA2026

A Fictitious Domain Formulation with Bubble Enrichment

Stefano Berrone, Lorenzo Neva, Stefano Scialò +1

The fictitious domain method offers a powerful alternative to circumvent complex mesh generation process by embedding the complex physical domain into a simpler, non-matching backg…

math.NA2024

A 3D-1D Virtual Element Method for Modeling Root Water Uptake

Stefano Berrone, Stefano Ferraris, Denise Grappein +2

An optimization-based strategy is proposed for coupling three-dimensional and one-dimensional problems (3D-1D coupling) in the context of soil-root interaction simulations. This st…

math.NA2024★ 4 cited

The lowest-order Neural Approximated Virtual Element Method on polygonal elements

Stefano Berrone, Moreno Pintore, Gioana Teora

The lowest-order Neural Approximated Virtual Element Method on polygonal elements is proposed here. This method employs a neural network to locally approximate the Virtual Element…

math.NA2024

A stabilized three fields formulation for Discrete Fracture Networks

Stefano Berrone, Silvia Bertoluzza, Stefano Scialò

We propose a hybridized domain decomposition formulation of the discrete fracture network model, allowing for independent discretization of the individual fractures. A natural norm…

math.NA2024★ 3 cited

3D Adaptive VEM with stabilization-free a posteriori error bounds

Stefano Berrone, Davide Fassino, Fabio Vicini

The present paper extends the theory of Adaptive Virtual Element Methods (AVEMs) to the three-dimensional meshes showing the possibility to bound the stabilization term by the resi…

math.NA2024★ 10 cited

Meshfree Variational Physics Informed Neural Networks (MF-VPINN): an adaptive training strategy

Stefano Berrone, Moreno Pintore

In this paper, we introduce a Meshfree Variational-Physics-Informed Neural Network. It is a Variational-Physics-Informed Neural Network that does not require the generation of the…