Issue |
ESAIM: COCV
Volume 27, 2021
Regular articles published in advance of the transition of the journal to Subscribe to Open (S2O). Free supplement sponsored by the Fonds National pour la Science Ouverte
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Article Number | S6 | |
Number of page(s) | 32 | |
DOI | https://doi.org/10.1051/cocv/2020052 | |
Published online | 01 March 2021 |
Shape optimization of a Dirichlet type energy for semilinear elliptic partial differential equations
1
Université de Lorraine, CNRS, Institut Elie Cartan de Lorraine, BP 70239,
54506
Vandœuvre-lès-Nancy Cedex, France.
2
Sorbonne Universités, UPMC Univ Paris 06, UMR 7598, Laboratoire Jacques-Louis Lions,
75005
Paris, France.
3
IRMA, Université de Strasbourg, CNRS UMR 7501, 7 rue René Descartes,
67084
Strasbourg, France.
* Corresponding author: mazari@math.cnrs.fr; idrissmazari@gmail.com
Received:
6
November
2019
Accepted:
22
July
2020
Minimizing the so-called “Dirichlet energy” with respect to the domain under a volume constraint is a standard problem in shape optimization which is now well understood. This article is devoted to a prototypal non-linear version of the problem, where one aims at minimizing a Dirichlet-type energy involving the solution to a semilinear elliptic PDE with respect to the domain, under a volume constraint. One of the main differences with the standard version of this problem rests upon the fact that the criterion to minimize does not write as the minimum of an energy, and thus most of the usual tools to analyze this problem cannot be used. By using a relaxed version of this problem, we first prove the existence of optimal shapes under several assumptions on the problem parameters. We then analyze the stability of the ball, expected to be a good candidate for solving the shape optimization problem, when the coefficients of the involved PDE are radially symmetric.
Mathematics Subject Classification: 49J45 / 49K20
Key words: Shape optimization / Dirichlet energy / existence/stability of optimal shapes
© The authors. Published by EDP Sciences, SMAI 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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