AI & ML
Obstacle Mean Curvature Flow: Efficient Approximation and Convergence Analysis
arXiv cs.NA0 views
arXiv:2512.16668v2 Announce Type: replace Abstract: We introduce a simple and efficient numerical method to compute mean curvature flow with obstacles. The method augments the Merrimam-Bence-Osher scheme with a pointwise update that enforces the constraint and therefore retains the computational complexity of the original scheme. Remarkably, this naive scheme inherits both crucial structural properties of obstacle mean curvature flow: a geometric comparison principle and a minimizing movements interpretation. The latter immediately implies the unconditional stability of the scheme. Based on the comparison principle we prove the convergence of the scheme to the viscosity solution of obstacle mean curvature flow. Moreover, using the minimizing movements interpretation, we show convergence of a spatially discrete model. Finally, we present numerical experiments for a physical model that inspired this work.
Source:https://arxiv.org/abs/2512.16668PreviousX-Safe: Embodiment-Agnostic Safeguarding for Robot Arm Manipulation Policies via Action MaskingNextA Thermo-Kinematic Operator Splitting Framework for Stefan Problems Based on Extreme Learning Machines
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