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doi: 10.4171/ifb/97
We propose in this paper a new algorithm for computing the evolution by mean curvature of a hypersurface. Our algorithm is a variant of the variational approach of Almgren, Taylor and Wang [2]. We show that it approximates, as the time-step goes to zero, the generalized motion(in the sense of barriers or viscosity solutions). The results still hold for the Anisotropic Mean Curvature Motion, as long as the anisotropy is smooth.
Mean Curvature Motion, algorithm, viscosity solutions, Asymptotic behavior of solutions to PDEs, anisotropy, Theoretical approximation in context of PDEs, Probabilités et mathématiques appliquées, 510, 004, 519, mean curvature motion, Nonlinear parabolic equations, Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems, Geometric evolution equations (mean curvature flow, Ricci flow, etc.)
Mean Curvature Motion, algorithm, viscosity solutions, Asymptotic behavior of solutions to PDEs, anisotropy, Theoretical approximation in context of PDEs, Probabilités et mathématiques appliquées, 510, 004, 519, mean curvature motion, Nonlinear parabolic equations, Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems, Geometric evolution equations (mean curvature flow, Ricci flow, etc.)
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 91 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |