
KARHU v2.0.0 This dataset consists of 38880 equilibria and ideal MHD stability evaluations. The dataset generation process is described in the related publication [A. Bruncrona, PoP (2025)]. The dataset is saved as a hdf5 file. For the KARHU training, one equilbrium is considered to consist of a pressure profile p, a safety factor profile q_s, a diamagnetic profile RB_phi, the shape of the plasma boundary and two scalars: the field and the radius at the magnetic axis, B_mag and R_mag. These are used as input to the KARHU model. Ideal MHD stability evaluations were done for multiple toroidal mode numbers per equilibrium, but KARHU v2.0 only predicts the growth rate of the most unstable mode (the maximum growthate over Ntors). However, all growth rates and Ntors are included in this dataset. The dataset also includes other parameters that are not used during the model training at all. There is also a group for resistive growthrates (CASTOR), however, this is still under development and most entries are empty. Top-level groups: karhu profiles scalars growthrates_mishka growthrates_castor (empty for most samples, to be implemented) Group: karhu psin_axis (64,): Describes the grid onto which the profiles are interpolated theta_axis (128,): Described the theta grid onto which the boundary is mapped (polar coordinates) Group: profile boundary_polar (38880, 2, 128): boundary shape in polar coordinates R(theta) p0 (38880, 64): pressure profiles qs (38880, 64): safety factor profiles rbphi (38880, 64): diamagnetic profiles Group: scalars b0 (38880,) ballooning_stable (38880,) betan (38880,) betap (38880,) bmag (38880,) bt (38880,) bvac (38880,) ip (38880,) max_gr_castor (38880,) max_gr_mishka (38880,) max_gr_ntor_castor (38880,) max_gr_ntor_mishka (38880,) mercier_stable (38880,) q_at_boundary (38880,) q_on_axis (38880,) radius (38880,) rmag (38880,) rvac (38880,) total_current (38880,) Group: growthrates_mishka gamma (38880,) ntor (38880,) Group: growthrates_castor gamma (38880,) ntor (38880,)
Magnetohydrodynamics, Machine learning, Nuclear fusion, Integrated modelling, Neural networks, Fusion physics, Plasma physics
Magnetohydrodynamics, Machine learning, Nuclear fusion, Integrated modelling, Neural networks, Fusion physics, Plasma physics
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