An a posteriori-driven adaptive mixed high-order method with application to electrostatics

Abstract : In this work we propose an adaptive version of the recently introduced Mixed High-Order method and showcase its performance on a comprehensive set of academic and industrial problems in computational electromagnetism. The latter include, in particular, the numerical modeling of comb-drive and MEMS devices. Mesh adaptation is driven by newly derived, residual-based error estimators. The resulting method has several advantageous features: It supports fairly general meshes, it enables arbitrary approximation orders, and has a moderate computational cost thanks to hybridization and static condensation. The a posteriori-driven mesh refinement is shown to significantly enhance the performance on problems featuring singular solutions, allowing to fully exploit the high-order of approximation.
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Journal of Computational Physics, Elsevier, 2016, 326, pp.35-55. 〈10.1016/j.jcp.2016.08.041〉
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Contributeur : Daniele Antonio Di Pietro <>
Soumis le : jeudi 22 septembre 2016 - 10:53:13
Dernière modification le : vendredi 14 décembre 2018 - 11:31:08

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Daniele Antonio Di Pietro, Ruben Specogna. An a posteriori-driven adaptive mixed high-order method with application to electrostatics. Journal of Computational Physics, Elsevier, 2016, 326, pp.35-55. 〈10.1016/j.jcp.2016.08.041〉. 〈hal-01310313v2〉

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