Giovanni Bellettini

Lecture Notes on Mean Curvature Flow: Barriers and Singular Perturbations

• Broschiertes Buch

Produktbeschreibung

The aim of the book is to study some aspects of geometric evolutions, such as mean curvature flow and anisotropic mean curvature flow of hypersurfaces. We analyze the origin of such flows and their geometric and variational nature. Some of the most important aspects of mean curvature flow are described, such as the comparison principle and its use in the definition of suitable weak solutions. The anisotropic evolutions, which can be considered as a generalization of mean curvature flow, are studied from the view point of Finsler geometry. Concerning singular perturbations, we discuss the convergence of the Allen-Cahn (or Ginsburg-Landau) type equations to (possibly anisotropic) mean curvature flow before the onset of singularities in the limit problem. We study such kinds of asymptotic problems also in the static case, showing convergence to prescribed curvature-type problems.

Produktdetails

• Lecture Notes (Scuola Normale Superiore)
• Verlag: Edizioni Della Normale / Scuola Normale Superiore / Springer, Berlin
• Artikelnr. des Verlages: 978-88-7642-428-1
• 2014
• Seitenzahl: 348
• Erscheinungstermin: 16. Januar 2014
• Englisch
• Abmessung: 235mm x 155mm x 19mm
• Gewicht: 528g
• ISBN-13: 9788876424281
• ISBN-10: 8876424288
• Artikelnr.: 38239950

Inhaltsangabe

Signed distance from a smooth boundary.- Mean curvature vector and second fundamental form.- First variations of volume integrals and of the perimeter.- Smooth mean curvature flows.- Huisken's monotonicity formula.- Inclusion principle. Local well posedness: the approach of Evans-Spruck.- Grayson's example.- De Giorgi's barriers.- Inner and outer regularizations.- An example of fattening.- Ilmanen's interposition lemma.- The avoidance principle.- Comparison between barriers and a generalized evolution.- Barriers and level set evolution.- Parabolic singular perturbations: formal matched asymptotics, convergence and error estimate.