FDOT: A Fast, memory-efficient and automated approach for Discrete adjoint sensitivity analysis using the Operator overloading Technique
A new toolbox based on operator overloading is introduced for automatic differentiation of scientific computing codes – and in particular legacy computational fluid dynamics solvers that are developed using Fortran. The method can be readily implemented into existing iterative solvers with minimal c...
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| Vydáno v: | Aerospace science and technology Ročník 91; s. 159 - 174 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Masson SAS
01.08.2019
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| ISSN: | 1270-9638, 1626-3219 |
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| Abstract | A new toolbox based on operator overloading is introduced for automatic differentiation of scientific computing codes – and in particular legacy computational fluid dynamics solvers that are developed using Fortran. The method can be readily implemented into existing iterative solvers with minimal changes to the primal code. The integrated toolbox can efficiently calculate the sensitivities of any objective function with respect to all variables (design or intermediate) that can later be used for gradient-based design optimization, uncertainty quantification, error estimation, and mesh adaptation. The underlying definition of the current automatic differentiation is directly related to the discrete adjoint sensitivity analysis. Unlike most traditional operator overloading-based adjoint approaches reported in the literature, the current technique offers huge reductions in the memory footprint. To demonstrate the advantages of the current approach, various solvers/problems are considered. It is shown that the proposed technique can be used as an efficient toolbox for automatic differentiation of scientific solvers requiring only a handful additional lines of coding. |
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| AbstractList | A new toolbox based on operator overloading is introduced for automatic differentiation of scientific computing codes – and in particular legacy computational fluid dynamics solvers that are developed using Fortran. The method can be readily implemented into existing iterative solvers with minimal changes to the primal code. The integrated toolbox can efficiently calculate the sensitivities of any objective function with respect to all variables (design or intermediate) that can later be used for gradient-based design optimization, uncertainty quantification, error estimation, and mesh adaptation. The underlying definition of the current automatic differentiation is directly related to the discrete adjoint sensitivity analysis. Unlike most traditional operator overloading-based adjoint approaches reported in the literature, the current technique offers huge reductions in the memory footprint. To demonstrate the advantages of the current approach, various solvers/problems are considered. It is shown that the proposed technique can be used as an efficient toolbox for automatic differentiation of scientific solvers requiring only a handful additional lines of coding. |
| Author | Ekici, Kivanc Djeddi, Reza |
| Author_xml | – sequence: 1 givenname: Reza surname: Djeddi fullname: Djeddi, Reza email: sdjeddi@vols.utk.edu – sequence: 2 givenname: Kivanc surname: Ekici fullname: Ekici, Kivanc email: ekici@utk.edu |
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| CitedBy_id | crossref_primary_10_1016_j_ast_2019_105546 crossref_primary_10_1016_j_ast_2021_106927 crossref_primary_10_1016_j_cma_2024_117442 crossref_primary_10_2514_1_J061267 crossref_primary_10_2514_1_J061476 crossref_primary_10_2514_1_J059505 crossref_primary_10_2514_1_J059923 crossref_primary_10_1016_j_ast_2023_108854 crossref_primary_10_2514_1_J060142 crossref_primary_10_1016_j_ast_2020_105955 |
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| Keywords | Discrete adjoint Automatic differentiation (AD) Sensitivity analysis Computational fluid dynamics Object-oriented programming Operator overloading |
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| Title | FDOT: A Fast, memory-efficient and automated approach for Discrete adjoint sensitivity analysis using the Operator overloading Technique |
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