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Convergence analysis of Galerkin finite element approximations to shape gradients in eigenvalue optimization

机译:Galerkin有限元近似对特征值优化形状梯度的收敛性分析

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This paper concerns the accuracy of Galerkin finite element approximations to two types of shape gradients for eigenvalue optimization. Under certain regularity assumptions on domains, a priori error estimates are obtained for the two approximate shape gradients. Our convergence analysis shows that the volume integral formula converges faster and offers higher accuracy than the boundary integral formula. Numerical experiments validate the theoretical results for the problem with a pure Dirichlet boundary condition. For the problem with a pure Neumann boundary condition, the boundary formulation numerically converges as fast as the distributed type.
机译:本文涉及Galerkin有限元近似为特征值优化的两种形状梯度的准确性。在域的某些规律性假设下,为两个近似形状梯度获得先验误差估计。我们的收敛分析表明,体积积分配方会更快地收敛并提供比边界积分公式更高的精度。数值实验用纯粹的Dirichlet边界条件验证了问题的理论结果。对于纯净Neumann边界条件的问题,边界配方数量地收敛像分布式的快速。

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