1998
DOI: 10.1080/03605309808821353
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Sobolev regularization of a nonlinear ill-posed parabolic problem as a model for aggregating populations

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Cited by 72 publications
(11 citation statements)
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“…The partial differential equation in problem (1-1) can be regarded as the regularization of the forwardbackward parabolic equation u t = ϕ(u), which leads to ill-posed problems. The latter equation and its regularizations arise in several applications, such as edge detection in image processing [Perona and Malik 1990], aggregation models in population dynamics [Padrón 1998], and stratified turbulent shear flow [Barenblatt et al 1993a].…”
Section: Introductionmentioning
confidence: 99%
“…The partial differential equation in problem (1-1) can be regarded as the regularization of the forwardbackward parabolic equation u t = ϕ(u), which leads to ill-posed problems. The latter equation and its regularizations arise in several applications, such as edge detection in image processing [Perona and Malik 1990], aggregation models in population dynamics [Padrón 1998], and stratified turbulent shear flow [Barenblatt et al 1993a].…”
Section: Introductionmentioning
confidence: 99%
“…where B = (I − ∆) −1 is a nonlocal operator [33]. According to the above two non-local effects, equations like (1) have had a high profile in the study of many phenomena such as biological species dynamics, nonlinear elasticity, non-stationary fluid, image recovery,... (see [28,1,5,6,21] and references therein). The third non-local term comes from the source |u| q−1 u − 1 |Ω| Ω |u| q−1 udx, which leads to the conservation property Ω u = 0, and points out that the solutions may change sign.…”
mentioning
confidence: 99%
“…The smoothing term in Equation 1 is required to regularize the solution in the presence of sparse data. Previously, a Sobolev smoothing term [12, 23] has been used for this purpose [6]; however, this has the disadvantage of being sensitive to large rotations and motions, as are often seen around the RV, leaving artifacts in the model surfaces. In this work we used a D-Affine smoothing term [5], which penalized change in deformation across the model.…”
Section: Methodsmentioning
confidence: 99%