AI Article Synopsis

  • The text introduces new algorithms designed for efficiently and accurately computing area-preserving motions of curves in the plane, specifically focusing on motions driven by curvature.
  • These algorithms utilize a novel class of diffusion-generated motion techniques that involve simple operations like Gaussian kernel convolution and distance function construction.
  • The findings include applications of these algorithms to large-scale simulations of coarsening processes, demonstrating their practical use in geometric flow analysis.

Article Abstract

We propose efficient and accurate algorithms for computing certain area preserving geometric motions of curves in the plane, such as area preserving motion by curvature. These schemes are based on a new class of diffusion generated motion algorithms using signed distance functions. In particular, they alternate two very simple and fast operations, namely convolution with the Gaussian kernel and construction of the distance function, to generate the desired geometric flow in an unconditionally stable manner. We present applications of these area preserving flows to large scale simulations of coarsening.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521610PMC
http://dx.doi.org/10.1137/100815542DOI Listing

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