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SICOPOLIS-AD v2

SICOPOLIS-AD v2 is an open-source adjoint and tangent linear modeling framework for ice sheet model SICOPOLIS enabled by the Automatic Differentiation (AD) tool Tapenade.

Find the documentation here.

The AD code is tested using Gitlab-CI. The testing setup can be found in the directory test_ad of the SICOPOLIS installation.

SICOPOLIS-AD v2 is an open source project that relies on the participation of its users, and we welcome contributions. Users can contribute using the usual pull request mechanisms in git, and if the contribution is substantial, they can contact us to discuss gaining direct access to the repository.

If you think you’ve found a bug, please check if you’re using the latest version of the model. If the bug is still present, then think about how you might fix it and file a ticket in the Gitlab issue tracker (you might need to request membership access on Gitlab, which we can approve). Your ticket should include: what the bug does, the location of the bug: file name and line number(s), and any suggestions you have for how it might be fixed.

To request a new feature, or guidance on how to implement it yourself, please open a ticket with a clear explanation of what the feature will do.

You can also directly contact Shreyas Gaikwad (shreyas.gaikwad@utexas.edu) for any of the above.

Installation

  • Tapenade 3.16 (or latest version)

  • SICOPOLIS-AD v2

    • Pre-requisites for smooth functioning

      1. GNU GCC compiler (gfortran, tested on 5.4.0 or newer)

      2. Git (for cloning the SICOPOLIS repo)

      3. Unix-like system

      4. LIS (External library similar to PETSc, 1.4.43 or newer)

      5. NetCDF (External library and machine-independent data format, 3.6.x or newer)

For more details, check the documentation here.

Capabilities

  • Gradient calculation enabled using

    1. Adjoint mode
    2. Tangent linear mode
    3. Finite differences mode

  • Automated python scripts for adjoint validation with finite differences

Potential Uses

  • Sensitivity analysis

  • Model calibration

  • Efficient uncertainty quantification

  • Optimal Experimental Design (OED)

Contributors

Shreyas Gaikwad, Sri Hari Krishna Narayanan, Laurent Hascoet, Liz Curry-Logan, Ralf Greve, Patrick Heimbach

For contributions of each author, please check the documentation here.

License

The software is hosted under the GNU General Public License.