Automatic differentiation and Navier-Stokes.

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Description

We describe the use of automatic differentiation (AD) to enhance a compressible Navier-Stokes model. With the solver, AD is used to accelerate convergence by more than an order of magnitude. Outside the solver, AD is used to compute the derivatives needed for optimization. We emphasize the potential for performance gains if the programmer does not treat AD as a black box, but instead utilizes high-level knowledge about the nature of the application.

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22 p.

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Bischof, C.; Hovland, P. & Mohammadi, B. December 17, 1997.

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Description

We describe the use of automatic differentiation (AD) to enhance a compressible Navier-Stokes model. With the solver, AD is used to accelerate convergence by more than an order of magnitude. Outside the solver, AD is used to compute the derivatives needed for optimization. We emphasize the potential for performance gains if the programmer does not treat AD as a black box, but instead utilizes high-level knowledge about the nature of the application.

Physical Description

22 p.

Notes

OSTI as DE00008919

Medium: P; Size: 22 pages

Source

  • Workshop on Optimal Design and Control, Arlington, VA (US), 09/29/1997--10/03/1997

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  • Report No.: ANL/MCS/CP-95126
  • Grant Number: W-31-109-ENG-38
  • Office of Scientific & Technical Information Report Number: 8919
  • Archival Resource Key: ark:/67531/metadc794518

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Office of Scientific & Technical Information Technical Reports

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Creation Date

  • December 17, 1997

Added to The UNT Digital Library

  • Dec. 19, 2015, 7:14 p.m.

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  • April 10, 2017, 3:13 p.m.

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Bischof, C.; Hovland, P. & Mohammadi, B. Automatic differentiation and Navier-Stokes., article, December 17, 1997; Illinois. (digital.library.unt.edu/ark:/67531/metadc794518/: accessed October 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.