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20222026
most citedThe 3D strict separation property for the nonlocal Cahn-Hilliard equation with singular potential

9 citations · 14 across the 24 of their papers we have counts for

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6 papers · 1 filter

math.AP2024

Long-Time behavior of the tangential surface Navier-Stokes equation

Andrea Poiatti, Ulisse Stefanelli

We investigate the initial-value problem for the incompressible tangential Navier-Stokes equation with variable viscosity on a given two-dimensional surface without boundary. Exist…

math.AP2024

Multi-component phase separation and small deformations of a spherical biomembrane

Diogo Caetano, Charles M. Elliott, Maurizio Grasselli +1

We focus on the derivation and analysis of a model for multi-component phase separation occurring on biological membranes, inspired by observations of lipid raft formation. The mod…

math.AP2024

Diffuse Interface Model for Two-Phase Flows on Evolving Surfaces with Different Densities: Global Well-Posedness

Helmut Abels, Harald Garcke, Andrea Poiatti

We show existence and uniqueness of strong solutions to a Navier-Stokes/Cahn-Hilliard type system on a given two-dimensional evolving surface in the case of different densities and…

math.AP2024

Diffuse Interface Model for Two-Phase Flows on Evolving Surfaces with Different Densities: Local Well-Posedness

Helmut Abels, Harald Garcke, Andrea Poiatti

A Cahn-Hilliard-Navier-Stokes system for two-phase flow on an evolving surface with non-matched densities is derived using methods from rational thermodynamics. For a Cahn-Hilliard…

math.AP2024

Nonlocal-to-local convergence rates for strong solutions to a Navier-Stokes-Cahn-Hilliard system with singular potential

Christoph Hurm, Patrik Knopf, Andrea Poiatti

The main goal of this paper is to establish the nonlocal-to-local convergence of strong solutions to a Navier--Stokes--Cahn--Hilliard model with singular potential describing immis…

math.NA2024

Structure-preserving neural networks in data-driven rheological models

Nicola Parolini, Andrea Poiatti, Julian Vene' +1

In this paper we address the importance and the impact of employing structure preserving neural networks as surrogate of the analytical physics-based models typically employed to d…