Changes (by Zach Etienne):

Update (May 2020): The original description below is a bit outdated, as BaikalETK was restructured (for the better!) a bit after ETK telecon discussions. For one, BaikalETK is now two thorns: Baikal and BaikalVacuum, where the former includes stress-energy source terms and the latter does not. Also the former contains 2nd and 4th order finite-difference derivative C code kernels and the latter includes 4th, 6th, and 8th order kernels. These may be specified at runtime, so the user need not rerun NRPy+ to generate the most commonly used kernels. Still for maximum flexibility one would need to run NRPy+ to generate kernels that are not already provided. For more details please read the documentation linked to below, and do check out the contents of the doc/ directories of each thorn.

BaikalETK is an open-source NRPy+-based BSSN thorn, which has been demonstrated to yield excellent agreement with ML_BSSN when evolving BBHs and BNSs, and in certain cases (e.g., ADM mass volume integral over entire grid vs time in a pre-merged BNS simulation) better results than ML_BSSN.

BaikalETK is BSD 2-clause open-source licensed and available in the NRPy+ github page (https://github.com/zachetienne/nrpytutorial). One can browse its source code & documentation with nbviewer: https://nbviewer.jupyter.org/github/zachetienne/nrpytutorial/blob/master/Tutorial-BaikalETK.ipynb

BaikalETK has the following neat

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Ticket URL: https://bitbucket.org/einsteintoolkit/tickets/issues/2299/proposal-add-baikaletk-to-the-toolkit