Showing papers by "Mykhaylo Shkolnikov published in 2023"

Deep Level-set Method for Stefan Problems

Abstract: We propose a level-set approach to characterize the region occupied by the solid in Stefan problems with and without surface tension, based on their recent probabilistic reformulation. The level-set function is parameterized by a feed-forward neural network, whose parameters are trained using the probabilistic formulation of the Stefan growth condition. The algorithm can handle Stefan problems where the liquid is supercooled and can capture surface tension effects through the simulation of particles along the moving boundary together with an efficient approximation of the mean curvature. We demonstrate the effectiveness of the method on a variety of examples with and without radial symmetry.

Well-posedness of the supercooled Stefan problem with oscillatory initial conditions

Abstract: We study the one-phase one-dimensional supercooled Stefan problem with oscillatory initial conditions. In this context, the global existence of so-called physical solutions has been shown recently in [CRSF20], despite the presence of blow-ups in the freezing rate. On the other hand, for regular initial conditions, the uniqueness of physical solutions has been established in [DNS22]. Here, we prove the uniqueness of physical solutions for oscillatory initial conditions by a new contraction argument that allows to replace the local monotonicity condition of [DNS22] with an averaging condition. We verify this weaker condition for fairly general oscillating probability densities, such as the ones given by an almost sure trajectory of $(1+W_x-\sqrt{2x|\log{|\log{x}|}|})_{+}\wedge 1$ near the origin, where $W$ is a standard Brownian motion. We also permit typical deterministically constructed oscillating densities, including those of the form $(1+\sin{1/x})/2$ near the origin. Finally, we provide an example of oscillating densities for which it is possible to go beyond our main assumption via further complementary arguments.