Automating embedded analysis capabilities and managing software complexity in multiphysics simulation, Part II: Application to partial differential equations

  • Authors:

  • Roger P. Pawlowski;Eric T. Phipps;Andrew G. Salinger;Steven J. Owen;Christopher M. Siefert;Matthew L. Staten

  • Affiliations:

  • Department of Numerical Analysis and Applications, Sandia National Laboratories, Albuquerque, NM, USA;Department of Numerical Analysis and Applications, Sandia National Laboratories, Albuquerque, NM, USA;Department of Numerical Analysis and Applications, Sandia National Laboratories, Albuquerque, NM, USA;Department of Numerical Analysis and Applications, Sandia National Laboratories, Albuquerque, NM, USA;Department of Numerical Analysis and Applications, Sandia National Laboratories, Albuquerque, NM, USA;Department of Numerical Analysis and Applications, Sandia National Laboratories, Albuquerque, NM, USA

  • Venue:
  • Scientific Programming
  • Year:
  • 2012

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Abstract

A template-based generic programming approach was presented in Part I of this series of papers [Sci. Program. 20 2012, 197--219] that separates the development effort of programming a physical model from that of computing additional quantities, such as derivatives, needed for embedded analysis algorithms. In this paper, we describe the implementation details for using the template-based generic programming approach for simulation and analysis of partial differential equations PDEs. We detail several of the hurdles that we have encountered, and some of the software infrastructure developed to overcome them. We end with a demonstration where we present shape optimization and uncertainty quantification results for a 3D PDE application.