Physics-regularized neural network of the ideal-MHD solution operator in Wendelstein 7-X configurations
نویسندگان
چکیده
Abstract The computational cost of constructing 3D magnetohydrodynamic (MHD) equilibria is one the limiting factors in stellarator research and design. Although data-driven approaches have been proposed to provide fast MHD equilibria, accuracy with which equilibrium properties are reconstructed unknown. In this work, we describe an artificial neural network (NN) that quickly approximates ideal-MHD solution operator Wendelstein 7-X (W7-X) configurations. This model fulfils symmetries by construction. force residual regularizes NN satisfy equations. predicts high accuracy, it faithfully reconstructs global quantities proxy functions used optimization. We also optimize W7-X magnetic configurations, where desiderable configurations can be found terms particle confinement. work demonstrates models approximate reconstruct interest, suggests how they might
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ژورنال
عنوان ژورنال: Nuclear Fusion
سال: 2023
ISSN: ['0029-5515', '1741-4326']
DOI: https://doi.org/10.1088/1741-4326/acc852