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Using Self-adjoint Extensions in Shape Optimization

机译:使用形状优化中的自相伴随扩展

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Self-adjoint extensions of elliptic operators are used to model the solution of a partial differential equation defined in a singularly perturbed domain. The asymptotic expansion of the solution of a Laplacian with respect to a small parameter e is first performed in a domain perturbed by the creation of a small hole. The resulting singular perturbation is approximated by choosing an appropriate self-adjoint extension of the Laplacian, according to the previous asymptotic analysis. The sensitivity with respect to the position of the center of the small hole is then studied for a class of functionals depending on the domain. A numerical application for solving an inverse problem is presented. Error estimates are provided and a link to the notion of topological derivative is established.
机译:椭圆算子的自相伴随扩展用于模拟在奇异扰动域中定义的部分微分方程的解。首先在由创建一个小孔的域中进行拉普拉斯溶液对小参数E的脱落膨胀。根据先前的渐近分析,通过选择Laplacian的适当自伴随延伸来近似得到的奇异扰动。然后根据域研究关于小孔的中心的位置的位置的灵敏度,这是一类功能。介绍了解决反问题的数值应用。提供错误估计,并建立了拓扑衍生物概念的链接。

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