Polygons as maximizers of Dirichlet energy or first eigenvalue of Dirichlet-Laplacian among convex planar domains
Jimmy Lamboley (SU), Arian Novruzi, Michel Pierre (ENS Rennes)

TL;DR
This paper proves that solutions to certain shape optimization problems with convexity constraints in the plane are polygons, focusing on maximizing Dirichlet energy and first eigenvalue of the Laplacian, with new results on shape derivatives.
Contribution
It establishes a weak convexity property for Dirichlet energy and eigenvalues in convex planar shapes, enabling the proof that optimal shapes are polygons.
Findings
Optimal convex shapes for certain functionals are polygons.
New estimates of second order shape derivatives for non-smooth convex shapes.
Convexity constraints lead to polygonal maximizers in shape optimization problems.
Abstract
In this paper we prove that solutions to several shape optimization problems in the plane, with a convexity constraint on the admissible domains, are polygons. The main terms of the shape functionals we consider are either E f (), the Dirichlet energy of the Laplacian in the domain , or 1 (), the first eigenvalue of the Dirichlet-Laplacian. Usually, one considers minimization of such functionals (often with measure constraint), as for example for the famous Saint-Venant and Faber-Krahn inequalities. By adding the convexity constraint (and possibly other constraints to ensure existence of an optimal shape) one allows to consider the rather unusual and difficult question of maximizing these functionals. This paper follows a series of papers by the authors, where the leading idea is that a certain concavity property of the shape functional that is…
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Taxonomy
TopicsNonlinear Partial Differential Equations · Advanced Mathematical Modeling in Engineering · Topology Optimization in Engineering
