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| Main Authors: | , , , |
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| Format: | Preprint |
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2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2004.06783 |
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| _version_ | 1866915900142125056 |
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| author | Gangl, Peter Sturm, Kevin Neunteufel, Michael Schöberl, Joachim |
| author_facet | Gangl, Peter Sturm, Kevin Neunteufel, Michael Schöberl, Joachim |
| contents | In this paper we present a framework for automated shape differentiation in the finite element software NGSolve. Our approach combines the mathematical Lagrangian approach for differentiating PDE constrained shape functions with the automated differentiation capabilities of NGSolve. The user can decide which degree of automatisation is required and thus allows for either a more custom-like or black-box-like behaviour of the software.
We discuss the automatic generation of first and second order shape derivatives for unconstrained model problems as well as for more realistic problems that are constrained by different types of partial differential equations. We consider linear as well as nonlinear problems and also problems which are posed on surfaces. In numerical experiments we verify the accuracy of the computed derivatives via a Taylor test. Finally we present first and second order shape optimisation algorithms and illustrate them for several numerical optimisation examples ranging from nonlinear elasticity to Maxwell's equations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2004_06783 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Fully and Semi-Automated Shape Differentiation in NGSolve Gangl, Peter Sturm, Kevin Neunteufel, Michael Schöberl, Joachim Optimization and Control In this paper we present a framework for automated shape differentiation in the finite element software NGSolve. Our approach combines the mathematical Lagrangian approach for differentiating PDE constrained shape functions with the automated differentiation capabilities of NGSolve. The user can decide which degree of automatisation is required and thus allows for either a more custom-like or black-box-like behaviour of the software. We discuss the automatic generation of first and second order shape derivatives for unconstrained model problems as well as for more realistic problems that are constrained by different types of partial differential equations. We consider linear as well as nonlinear problems and also problems which are posed on surfaces. In numerical experiments we verify the accuracy of the computed derivatives via a Taylor test. Finally we present first and second order shape optimisation algorithms and illustrate them for several numerical optimisation examples ranging from nonlinear elasticity to Maxwell's equations. |
| title | Fully and Semi-Automated Shape Differentiation in NGSolve |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2004.06783 |