QLKNN11D training set

QLKNN11D training set This dataset contains a large-scale run of ~1 billion flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at https://gitlab.com/qualikiz-group/QuaLiKiz/. This dataset was generated with the 'QLKNN11D-hyper' tag of QuaLiKiz, equivalent to 2.8.1 apart from the negative magnetic shear filter being disabled. See https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/QLKNN11D-hyper for the in-repository tag. The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See https://doi.org/10.1063/1.5134126 for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on arXiv https://arxiv.org/abs/1911.05617 and the older dataset on Zenodo https://doi.org/10.5281/zenodo.3497066. For an application example, see Van Mulders et al 2021 https://doi.org/10.1088/1741-4326/ac0d12, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. For any learned surrogates developed for QLKNN11D, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended. Related repositories: General QuaLiKiz documentation https://qualikiz.com QuaLiKiz/QLKNN input/output variables naming scheme https://qualikiz.com/QuaLiKiz/Input-and-output-variables Training, plotting, filtering, and auxiliary tools https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop QuaLiKiz related tools https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools FORTRAN QLKNN implementation with wrapper for Python and MATLAB https://gitlab.com/qualikiz-group/QLKNN-fortran Weights and biases of 'hyperrectangle style' QLKNN https://gitlab.com/qualikiz-group/qlknn-hype Data exploration The data is provided in 43 netCDF files. We advise opening single datasets using xarray or multiple datasets out-of-core using dask. For reference, we give the load times and sizes of a single variable that just depends on the scan size `dimx` below. This was tested single-core on a Intel Xeon 8160 CPU at 2.1 GHz and 192 GB of DDR4 RAM. Note that during loading, more memory is needed than the final number. Timing of dataset loading Amount of datasets Final in-RAM memory (GiB) Loading time single var (M:SS) 1 10.3 0:09 5 43.9 1:00 10 63.2 2:01 16 98.0 3:25 17 Out Of Memory x:xx Full dataset The full dataset of QuaLiKiz in-and-output data is available on request. Note that this is 2.2 TiB of netCDF files

QLKNN7D-edge training set

QLKNN7D-edge training set This dataset contains a large-scale run of ~15 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The dataset is in a parameter regime typical of the L-mode near edge (pedestal forming region). QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at https://gitlab.com/qualikiz-group/QuaLiKiz/. This dataset was generated with QuaLiKiz 2.8.4, which includes numerical improvements increasing the robustness of strongly driven (high gradient) calculations typical of the L-mode near-edge. See https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/2.8.4 for the in-repository tag. The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See https://doi.org/10.1063/1.5134126 for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on arXiv and the older dataset on Zenodo. For an application example, see Van Mulders et al 2021, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. An additional, larger, QuaLiKiz dataset is found at https://zenodo.org/record/8017522. Neither the QLKNN10D or QLKNN11D datasets include L-mode near-edge parameters. For any learned surrogates developed for QLKNN7D-edge, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended. Related repositories: General QuaLiKiz documentation QuaLiKiz/QLKNN input/output variables naming scheme Training, plotting, filtering, and auxiliary tools QuaLiKiz related tools FORTRAN QLKNN implementation with wrapper for Python and MATLAB Weights and biases of 'hyperrectangle style' QLKN