Regularisation and separation for evolving surface Cahn-Hilliard equations
Diogo Caetano, Charles M. Elliott, Maurizio Grasselli, Andrea Poiatti

TL;DR
This paper investigates the regularisation and separation properties of solutions to the evolving surface Cahn-Hilliard equation with logarithmic potential, establishing higher regularity and validating the double-well approximation on two-dimensional surfaces.
Contribution
It extends well-posedness and regularity results for the Cahn-Hilliard equation to evolving surfaces, proving separation properties and higher-order regularity.
Findings
Solutions stay uniformly away from pure phases after positive time
Regularisation properties of weak solutions are established
Validation of the double-well approximation on evolving surfaces
Abstract
We consider the Cahn-Hilliard equation with constant mobility and logarithmic potential on a two-dimensional evolving closed surface embedded in , as well as a related weighted model. The well-posedness of weak solutions for the corresponding initial value problems on a given time interval have already been established by the first two authors. Here we first prove some regularisation properties of weak solutions in finite time. Then, we show the validity of the strict separation property for both the problems. This means that the solutions stay uniformly away from the pure phases from any positive time on. This property plays an essential role to achieve higher-order regularity for the solutions. Also, it is a rigorous validation of the standard double-well approximation. The present results are a twofold extension of the well-known ones for the classical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSolidification and crystal growth phenomena · Advanced Mathematical Modeling in Engineering · Stochastic processes and statistical mechanics
