QLKNN7D-edge training set

<p><strong>QLKNN7D-edge training set</strong></p> <p>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 <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/">https://gitlab.com/qualikiz-group/QuaLiKiz/</a>. 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 <a href="https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/2.8.4">https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/2.8.4</a> for the in-repository tag.</p> <p>The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See <a href="https://doi.org/10.1063/1.5134126">https://doi.org/10.1063/1.5134126</a> 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 <a href="https://arxiv.org/abs/1911.05617">arXiv</a> and the older dataset on <a href="https://doi.org/10.5281/zenodo.3497066">Zenodo</a>. For an application example, see <a href="http://https://doi.org/10.1088/1741-4326/ac0d12">Van Mulders et al 2021</a>, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. An additional, larger, QuaLiKiz dataset is found at <a href="https://zenodo.org/record/8017522">https://zenodo.org/record/8017522</a>. 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.</p> <p>Related repositories:</p> <ul> <li><a href="https://qualikiz.com">General QuaLiKiz documentation </a></li> <li><a href="https://qualikiz.com/QuaLiKiz/Input-and-output-variables">QuaLiKiz/QLKNN input/output variables naming scheme </a></li> <li><a href="https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop">Training, plotting, filtering, and auxiliary tools </a></li> <li><a href="https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools">QuaLiKiz related tools </a></li> <li><a href="https://gitlab.com/qualikiz-group/QLKNN-fortran">FORTRAN QLKNN implementation with wrapper for Python and MATLAB </a></li> <li><a href="https://gitlab.com/qualikiz-group/qlknn-hyper">Weights and biases of &#39;hyperrectangle style&#39; QLKNN </a></li> </ul> <p>&nbsp;</p>