Topological Derivatives of Shape Functionals. Part III: Second-Order Method and Applications

Antonio André Novotny (), Jan Sokołowski () and Antoni Żochowski ()
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Antonio André Novotny: Coordenação de Matemática Aplicada e Computacional
Jan Sokołowski: Université de Lorraine
Antoni Żochowski: Polish Academy of Sciences

Journal of Optimization Theory and Applications, 2019, vol. 181, issue 1, No 1, 22 pages

Abstract: Abstract The framework of asymptotic analysis in singularly perturbed geometrical domains presented in the first part of this series of review papers can be employed to produce two-term asymptotic expansions for a class of shape functionals. In Part II (Novotny et al. in J Optim Theory Appl 180(3):1–30, 2019), one-term expansions of functionals are required for algorithms of shape-topological optimization. Such an approach corresponds to the simple gradient method in shape optimization. The Newton method of shape optimization can be replaced, for shape-topology optimization, by two-term expansions of shape functionals. Thus, the resulting approximations are more precise and the associated numerical methods are much more complex compared to one-term expansion topological derivative algorithms. In particular, numerical algorithms associated with first-order topological derivatives of shape functionals have been presented in Part II (Novotny et al. 2019), together with an account of their applications currently found in the literature, with emphasis on shape and topology optimization. In this last part of the review, second-order topological derivatives are introduced. Second-order algorithms of shape-topological optimization are used for numerical solution of representative examples of inverse reconstruction problems. The main feature of these algorithms is that the method is non-iterative and thus very robust with respect to noisy data as well as independent of initial guesses.

Keywords: Topological derivatives; Second-order method; Applications in inverse problems; 35J15; 35Q74; 49J20; 49M15; 49N45 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10957-018-1420-4

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