The topological derivative is, conceptually, a derivative of a shape functional with respect to infinitesimal changes in its topology, such as adding an infinitesimal hole or crack.
When used in higher dimensions than one, the term topological gradient is also used to name the first-order term of the topological asymptotic expansion, dealing only with infinitesimal singular domain perturbations.
, which is subject to a nonsmooth perturbation confined in a small region
is a positive first order correction function of
The topological derivative can be applied to shape optimization problems in structural mechanics.
It is a generalization of this classical tool in shape optimization.
The topological derivative technique can be coupled with level-set method.
[5] In 2005, the topological asymptotic expansion for the Laplace equation with respect to the insertion of a short crack inside a plane domain had been found.
It allows to detect and locate cracks for a simple model problem: the steady-state heat equation with the heat flux imposed and the temperature measured on the boundary.
[6] The topological derivative had been fully developed for a wide range of second-order differential operators and in 2011, it had been applied to Kirchhoff plate bending problem with a fourth-order operator.
[7] In the field of image processing, in 2006, the topological derivative has been used to perform edge detection and image restoration.
The impact of an insulating crack in the domain is studied.
The topological sensitivity gives information on the image edges.
The presented algorithm is non-iterative and thanks to the use of spectral methods has a short computing time.
[9] During the following years, other problems have been considered: classification, segmentation, inpainting and super-resolution.
[9][10][11][12][13] This approach can be applied to gray-level or color images.
The topological gradient is also able to provide edge orientation and this information can be used to perform anisotropic diffusion.
[15] In 2012, a general framework is presented to reconstruct an image
depends on the specific application as well as the linear observation operator
The idea to recover the original image is to minimize the following functional for
The first term of the equation ensures that the recovered image
is regular, and the second term measures the discrepancy with the data.
In this general framework, different types of image reconstruction can be performed such as[13] In this framework, the asymptotic expansion of the cost function
in the case of a crack provides the same topological derivative
[13] Thanks to the topological gradient, it is possible to detect the edges and their orientation and to define an appropriate
[13] In image processing, the topological derivatives have also been studied in the case of a multiplicative noise of gamma law or in presence of Poissonian statistics.
[16] In 2009, the topological gradient method has been applied to tomographic reconstruction.
[17] The coupling between the topological derivative and the level set has also been investigated in this application.
[18] In 2023, topological derivative was used to optimize shapes for inverse rendering.
A. Novotny and J. Sokolowski, Topological derivatives in shape optimization, Springer, 2013.