Nonlinear Gyrokinetic Simulations of Turbulence in Diverse Stellarator Geometries
Explore a new dataset of over 200,000 nonlinear gyrokinetic simulations of Ion Temperature Gradient (ITG) turbulence in various stellarator geometries. The dataset, with a high R-squared of 0.989, enables predictions of heat flux using neural networks and interpretable machine learning. Additionally, it facilitates testing of proposed turbulence cost functions. Paper details and dataset DOI included.
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New dataset of >200,000 nonlinear gyrokinetic simulations of ITG turbulence in diverse stellarator geometries Turbulence simulations M Landreman, J Y Choi, C Alves, P Balaprakash, R M Churchill , R Conlin, G Roberg-Clark arXiv:2502.11657
New dataset of >200,000 nonlinear gyrokinetic simulations of ITG turbulence in diverse stellarator geometries R2 = 0.989 Turbulence simulations Predicted heat flux Neural network True heat flux M Landreman, J Y Choi, C Alves, P Balaprakash, R M Churchill , R Conlin, G Roberg-Clark arXiv:2502.11657
New dataset of >200,000 nonlinear gyrokinetic simulations of ITG turbulence in diverse stellarator geometries R2 = 0.989 Turbulence simulations Predicted heat flux Neural network Interpretable machine learning True heat flux Flux surface compression Bad curvature M Landreman, J Y Choi, C Alves, P Balaprakash, R M Churchill , R Conlin, G Roberg-Clark arXiv:2502.11657
The data can also be used to test proposed turbulence cost functions
Paper: arXiv:2502.11657 Dataset doi:10.5281/zenodo.14867776 R2 = 0.989 Turbulence simulations Predicted heat flux Neural network Interpretable machine learning True heat flux Flux surface compression Bad curvature
