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Merge branch 'master' of https://github.com/spectralDNS/shenfun
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mikaem committed Jun 29, 2024
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6 changes: 1 addition & 5 deletions .devcontainer/devcontainer.json
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"vscode": {
"extensions": [
"ms-python.python"
],
"settings": {
"python.pythonPath": "/opt/conda/envs/shenfun",
"python.defaultInterpreterPath": "/opt/conda/envs/shenfun"
}
]
}
},
"postCreateCommand": "conda init"
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19 changes: 16 additions & 3 deletions README.rst
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Expand Up @@ -45,6 +45,19 @@ Shenfun can also be used to approximate analytical functions with global spectra
.. image:: https://cdn.jsdelivr.net/gh/spectralDNS/spectralutilities@master/figures/seashell3.png
:alt: The surface of a seashell

Some recent papers using Shenfun
---------------------------

- `Effective control of two-dimensional Rayleigh–Bénard convection: Invariant multi-agent reinforcement learning is all you need <https://pubs.aip.org/aip/pof/article/35/6/065146/2900730>`_ C. Vignon, J. Rabault, J. Vasanth, F. Alcántara-Ávila, M. Mortensen, R. Vinuesa, Physics of Fluids 35, 065146 (2023)
- `Solving Partial Differential Equations with Equivariant Extreme Learning Machines <https://www.researchgate.net/profile/Sebastian-Peitz/publication/380897446_Solving_Partial_Differential_Equations_with_Equivariant_Extreme_Learning_Machines/links/66544d0fbc86444c7205cbdb/Solving-Partial-Differential-Equations-with-Equivariant-Extreme-Learning-Machines.pdf>`_, H. Harder, J. Rabault, R. Vinuesa, M. Mortensen, S. Peitz. preprint (2024)
- `A global spectral-Galerkin investigation of a Rayleigh–Taylor instability in plasma using an MHD–Boussinesq model <https://pubs.aip.org/aip/adv/article/13/10/105319/2917415>`_ A. Piterskaya, Wojciech J. Miloch, M. Mortensen, AIP Advances 13, 105319 (2023)
- `A Generic and Strictly Banded Spectral Petrov–Galerkin Method for Differential Equations with Polynomial Coefficients <https://epubs.siam.org/doi/full/10.1137/22M1492842>`_ M. Mortensen, SIAM J. on Scientific Computing, 45, 1, A123-A146, (2023)
- `Variance representations and convergence rates for data-driven approximations of Koopman operators <https://arxiv.org/abs/2402.02494>`_ F. M. Philipp, M. Schaller, S. Boshoff, S. Peitz, F. Nüske, K. Worthmann, preprint (2024)
- `Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems <https://arxiv.org/abs/2307.15325>`_, S. Peitz, H. Harder, F. Nüske, F. Philipp, M. Schaller, K. Worthmann, preprint (2024)
- `Koopman-Based Surrogate Modelling of Turbulent Rayleigh-Bénard Convection <https://arxiv.org/abs/2405.06425>`_ T. Markmann, M. Straat, B. Hammer, preprint (2024)
- `Shenfun: High performance spectral Galerkin computing platform <https://joss.theoj.org/papers/10.21105/joss.01071.pdf>`_, M. Mortensen, Journal of Open Source Software, 3(31), 1071 (2018)


Installation
------------

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---------------------
The easiest way to try out shenfun is to create your own codespace. Press the green codespace button on this page and wait for a couple of minutes while everything in `environment.yml` gets installed. Then write in the terminal of the codespace editor::

source activate ./venv
python setup.py build_ext -i
source activate shenfun
echo -e "PYTHONPATH=/workspaces/shenfun" > .env
export PYTHONPATH=/workspaces/shenfun

and you are set to run any of the demo programs, or for example try to follow the detailed instructions in the `documentation <https://shenfun.readthedocs.io/en/latest/gettingstarted.html>`_. We assume that you know how to run a Python program. Please note that if you want to use for example IPython or Jupyter in the codespace, then these need to be installed into the venv environment.
and you are set to run any of the demo programs, or for example try to follow the detailed instructions in the `documentation <https://shenfun.readthedocs.io/en/latest/gettingstarted.html>`_. We assume that you know how to run a Python program. Please note that if you want to use for example IPython or Jupyter in the codespace, then these need to be installed into the shenfun environment.

Contact
-------
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