Rhetorical Approaches in Environmental Communication

Schmitt, Casey R.; Mocatta, Gabi; Tate, Joanne Marras

doi:10.4324/9780367275204-7

Cited by 1 publication

(2 citation statements)

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“…Bayesian inference [26] 1 0 4 10 5 -10 8 Equilibrium reconstruction [4,25] 1 0 0 10 1 -10 4 Stellarator optimization [27] 1 0 3 10 4 -10 7…”

Section: Applicationmentioning

confidence: 99%

“…The high computational cost limits an exhaustive exploration of the use case input space. A parallel version of VMEC has recently been developed [23], however, for example, the wall-clock time of a single free boundary equilibrium reconstruction, both in the case of a stellarator and a tokamak scenario, is still on the order of hours [24,25].…”

Section: Introductionmentioning

confidence: 99%

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Proof of concept of a fast surrogate model of the VMEC code via neural networks in Wendelstein 7-X scenarios

et al. 2021

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In magnetic confinement fusion research, the achievement of high plasma pressure is key to reaching the goal of net energy production. The magnetohydrodynamic (MHD) model is used to self-consistently calculate the effects the plasma pressure induces on the magnetic field used to confine the plasma. Such MHD calculations-usually done computationally-serve as input for the assessment of a number of important physics questions. The variational moments equilibrium code (VMEC) is the most widely used to evaluate 3D ideal-MHD equilibria, as prominently present in stellarators. However, considering the computational cost, it is rarely used in large-scale or online applications (e.g. Bayesian scientific modeling, real-time plasma control). Access to fast MHD equilibria is a challenging problem in fusion research, one which machine learning could effectively address. In this paper, we present artificial neural network (NN) models able to quickly compute the equilibrium magnetic field of Wendelstein 7-X. Magnetic configurations that extensively cover the device operational space, and plasma profiles with volume-averaged normalized plasma pressure β (β = 2μ 0 p B 2 ) up to 5% and non-zero net toroidal current are included in the data set. By using convolutional layers, the spectral representation of the magnetic flux surfaces can be efficiently computed with a single network. To discover better models, a Bayesian hyper-parameter search is carried out, and 3D convolutional NNs are found to outperform feed-forward fully-connected NNs. The achieved normalized root-mean-squared error, the ratio between the regression error and the spread of the data, ranges from 1% to 20% across the different scenarios. The model inference time for a single equilibrium is on the order of milliseconds. Finally, this work shows the feasibility of a fast NN drop-in surrogate model for VMEC, and it opens up new operational scenarios where target applications could make use of magnetic equilibria at unprecedented scales.

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“…Bayesian inference [26] 1 0 4 10 5 -10 8 Equilibrium reconstruction [4,25] 1 0 0 10 1 -10 4 Stellarator optimization [27] 1 0 3 10 4 -10 7…”

Section: Applicationmentioning

confidence: 99%