Reconstruction of plasma equilibrium and separatrix using convolutional physics-informed neural operator

Bonotto, Matteo; Abate, Domenico; Pigatto, Leonardo

doi:10.1016/j.fusengdes.2024.114193

Fusion Engineering and Design

2024

DOI: 10.1016/j.fusengdes.2024.114193

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Reconstruction of plasma equilibrium and separatrix using convolutional physics-informed neural operator

Matteo Bonotto,

Domenico Abate,

Leonardo Pigatto

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2024

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Cited by 3 publications

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Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches

Sánchez-Villar,

Bai,

Bertelli

et al. 2024

Nucl. Fusion

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A real-time capable core ICRF heating model on NSTX and WEST is developed. The model is based on two nonlinear regression algorithms, the random forest ensemble of decision trees and the multilayer perceptron neural network. The algorithms are trained on TORIC ICRF spectrum solver simulations of the expected flat-top operation scenarios in NSTX and WEST assuming Maxwellian plasmas. The surrogate models are shown to successfully capture the multi-species core ICRF power absorption predicted by the original model for the high harmonic fast wave and the ion cyclotron minority heating schemes while reducing the computational time by six orders of magnitude. Although these models can be expanded, the achieved regression scoring, computational efficiency and increased model robustness suggest these strategies can be implemented into integrated modeling frameworks for real-time control applications.

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Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches

Sánchez-Villar,

Bai,

Bertelli

et al. 2024

Nucl. Fusion

View full text Add to dashboard Cite

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Real-time equilibrium reconstruction by multi-task learning neural network based on HL-3 tokamak

Zheng,

Yang,

Liu

et al. 2024

Nucl. Fusion

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A neural network model, EFITNN, has been developed capable of real-time magnetic equilibrium reconstruction based on HL-3 tokamak magnetic measurement signals. The model processes inputs from 68 channels of magnetic measurement data gathered from 1159 HL-3 experimental discharges, including plasma current, loop voltage, and the poloidal magnetic fields measured by equilibrium probes. The outputs of the model feature eight key plasma parameters, alongside high-resolution ($129\times129$) reconstructions of the toroidal current density $J_{\text P}$ and poloidal magnetic flux profiles $\Psi_{rz}$. Moreover, the network's architecture employs a multi-task learning structure, which enables the sharing of weights and mutual correction among different outputs, and lead to increase the model's accuracy by up to 32\%. The performance of EFITNN demonstrates remarkable consistency with the offline EFIT, achieving average $R^2 =$ 0.941, 0.997 and 0.959 for eight plasma parameters, $\Psi_{rz}$ and $J_{\text P}$, respectively. The model's robust generalization capabilities are particularly evident in its successful predictions of quasi-snowflake (QSF) divertor configurations and its adept handling of data from shot numbers or plasma current intervals not previously encountered during training. Compared to numerical methods, EFITNN significantly enhances computational efficiency with average computation time ranging from 0.08ms to 0.45ms, indicating its potential utility in real-time isoflux control and plasma profile management.

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Overlaps between industrial informatics and control, data acquisition and management in Big Science

Manduchi

2024

Front. Ind. Eng.

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Big Science applications require very large infrastructures and often involve different countries in order to achieve important scientific results or to find solutions to the major problems of mankind, such as finding a clean and endless source of energy. Big Science applications represent not only a scientific challenge, but also large engineering applications involving a wide range of technologies shared with other industrial applications. As a consequence there is a significant overlap in technologies and approaches between Big Science and Industry. In this paper, the overlap between Big Science and industrial applications will be presented in more detail under the control perspective, that is, by highlighting the common aspects between industrial informatics and the control, data acquisition and data management in large scientific applications.

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Reconstruction of plasma equilibrium and separatrix using convolutional physics-informed neural operator

Cited by 3 publications

References 44 publications

Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches

Real-time capable modeling of ICRF heating on NSTX and WEST via machine learning approaches

Real-time equilibrium reconstruction by multi-task learning neural network based on HL-3 tokamak

Overlaps between industrial informatics and control, data acquisition and management in Big Science

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