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The dolfin-adjoint project automatically derives the discrete adjoint and tangent linear models. From a forward model written in the Python interface to DOLFIN. Naumann (2011) states that. T]he automatic generation of optimal (in terms of robustness and efficiency) adjoint versions of large-scale simulation code is one of the great open challenges in the field of High-Performance Scientific Computing. The dolfin-adjoint project aims to solve this problem. We now offer Docker images to simplify the instal...

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Imperial College London

Patrick Farrell

Royal S●●●●●●●f Mines

Lo●●on , --, SW7 2BP

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The dolfin-adjoint project automatically derives the discrete adjoint and tangent linear models. From a forward model written in the Python interface to DOLFIN. Naumann (2011) states that. T]he automatic generation of optimal (in terms of robustness and efficiency) adjoint versions of large-scale simulation code is one of the great open challenges in the field of High-Performance Scientific Computing. The dolfin-adjoint project aims to solve this problem. We now offer Docker images to simplify the instal...
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3 download
4 citing
5 news
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7 with no modification
8 rigorous verification
9 solves optimisation problems
10 limitations
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dolfin-adjoint about | dolfin-adjoint.org Reviews

https://dolfin-adjoint.org

The dolfin-adjoint project automatically derives the discrete adjoint and tangent linear models. From a forward model written in the Python interface to DOLFIN. Naumann (2011) states that. T]he automatic generation of optimal (in terms of robustness and efficiency) adjoint versions of large-scale simulation code is one of the great open challenges in the field of High-Performance Scientific Computing. The dolfin-adjoint project aims to solve this problem. We now offer Docker images to simplify the instal...

INTERNAL PAGES

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1

dolfin-adjoint features

http://www.dolfin-adjoint.org/en/latest/features/index.html

Dolfin-adjoint works for both steady and time-dependent models, and for both linear and nonlinear models. The user interface is exactly the same in both cases. For an example of adjoining a nonlinear time-dependent model, see the. Dolfin-adjoint has been carefully designed to try to make its use as easy as possible. In many cases the only change to the forward model is to add. At the top of the model. For example, deriving the adjoint of. Requires adding precisely two lines to the forward model. Because ...

2

dolfin-adjoint about

http://www.dolfin-adjoint.org/en/latest/about/index.html

The dolfin-adjoint project automatically derives the discrete adjoint and tangent linear models. From a forward model written in the Python interface to DOLFIN. Naumann (2011) states that. T]he automatic generation of optimal (in terms of robustness and efficiency) adjoint versions of large-scale simulation code is one of the great open challenges in the field of High-Performance Scientific Computing. The dolfin-adjoint project aims to solve this problem. Dolfin-adjoint has the following features:. The a...

3

Support

http://www.dolfin-adjoint.org/en/latest/support/index.html

Do you wonder about how something works, whether some feature is supported or why you are getting an error? Feel free to ask on the bitbucket bugtracker. Checklist when asking a question. We want to help you; by making sure that you provide us with enough information, you are more likely to get the answer you are looking for. If you have encountered an error or a problem that you can’t figure out, please make sure to include the following in your description of the problem:. Minimal, running code example.

4

dolfin-adjoint documentation

http://www.dolfin-adjoint.org/en/latest/documentation/index.html

Documentation for dolfin-adjoint 2016.1. Have you just discovered dolfin-adjoint and want to get up and running quickly? Then, we suggest you. Start by going through the first steps of the dolfin-adjoint tutorial. Move on to look at the more detailed topology optimization example. Install dolfin-adjoint and its dependencies. Next, feel free to take a look at the complete introduction to dolfin-adjoint. An introduction to the mathematical background of adjoints. Or the dolfin-adjoint API reference.

5

Downloading dolfin-adjoint

http://www.dolfin-adjoint.org/en/latest/download/index.html

Docker images (all platforms and versions). Allows us to build and ship consistent high-performance dolfin-adjoint installations with FEniCS for almost any platform. To get started, follow these 2 steps:. Install Docker. Mac and Windows users should install the Docker Toolbox. This is a simple one-click install) and Linux users should follow these instructions. Install the FEniCS Docker script:. Curl -s https:/ get.fenicsproject.org bash. Fenicsproject run quay.io/dolfinadjoint/dolfin-adjoint. This is a ...

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About: Marie E. Rognes

http://home.simula.no/~meg

CV (07.12.15). Marie E. Rognes, Ph.D. Head of Department, Biomedical Computing. Chief Research Scientist, Scientific Computing, Simula Research Laboratory. Adjunct Associate Professor, Department of Mathematics. Our new paper on on Astrocytic ion dynamics: implications for Potassium buffering and liquid flow. G Halnes, K. Pettersen, L. Oeyehaug, M. E. Rognes, H. P. Langtangen and G. Einevoll) is now on arXiv and accepted for publication. In Voss, Sept 24-29, 2017. At Svalbard, Feb 6-10, 2017. Patrick Far...

nag.co.uk nag.co.uk

The Wilkinson Prize for Numerical Software www.nag.co.uk

http://www.nag.co.uk/other/WilkinsonPrize.html

Skip to main content. Europe, UK and Headquarters. Oxford OX2 8DR, UK. 801 Warrenville Road, Suite 185. Lisle, IL 60532-4332, USA. Hatchobori Frontier Building 2F. 4-9-9 Hatchobori, Chuo-ku. Tokyo 104-0032, JAPAN. Business Intelligence and Analytics Solutions. Oil and Gas Industry Solutions. Independent Software Vendor Solutions. Sign up to our newsletter. Algorithm Design and Development. Software Porting and Tuning. Sign up to our newsletter. NAG C Library - Latest Content. NAG Library for Python.

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Applications of the FEniCS Project — FEniCS Project

https://fenicsproject.org/applications

Applications of the FEniCS Project. This page highlights a number of FEniCS based applications offered as part of FEniCS Apps. If you have developed a FEniCS based application that you would like us to include in this list, make a request on the FEniCS mailing list at fenics-dev @. ASCoT — Automated Stability Condition Tester. CBCBlock — Block linear algebra in Python. CBCPDESys — Specify large systems of PDEs with ease. CBCPDESys is a Python package for specifying and solving large, completely general, ...

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FEniCS ‘15 — Firedrake 0.13.0+505.g425cfbc documentation

http://firedrakeproject.org/fenics_15.html

Is proud to host the 2015 FEniCS Workshop. The workshop is an opportunity for all those interested the FEniCS Project. And related projects such as dolfin-adjoint. To exchange ideas, communicate their results and network with the automated finite element community. The workshop welcomes existing and potential developers and users of automated simulation systems, as well as mathematicians interested in a high productivity computational environment for finite element problems. The schedule is now online.

optimojoe.com optimojoe.com

HPC considerations for robust, reliable optimization codes -- OptimoJoe

http://www.optimojoe.com/post/hpc-considerations-for-optimization-codes

HPC Considerations For Robust, Reliable Optimization Codes. A Discussion About Practical Considerations When Integrating Optimization Codes Into HPC Applications. Posted by Joseph Young. On Tue, Jul 7, 2015. It’s been awhile since I’ve had an update, but rest assured that there’s been lots of activity. To give some back story, OptimoJoe’s. Primary business is creating custom codes to solve a variety of engineering and scientific problems. Often, we use Optizelle. Involved getting the interior point algor...

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dolfin-adjoint about

The dolfin-adjoint project automatically derives the discrete adjoint and tangent linear models. From a forward model written in the Python interface to DOLFIN. Naumann (2011) states that. T]he automatic generation of optimal (in terms of robustness and efficiency) adjoint versions of large-scale simulation code is one of the great open challenges in the field of High-Performance Scientific Computing. The dolfin-adjoint project aims to solve this problem. We now offer Docker images to simplify the instal...

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