Investigating the thermal stability of highly radiative discharges on JET with a new tomographic method

Murari, A.; Peluso, E.; Lowry, C.; Aleiferis, S.; Carvalho, P.; Gelfusa, M.; Contributors, Jet

doi:10.1088/1741-4326/ab7536

Cited by 17 publications

(18 citation statements)

References 19 publications

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“…Moreover, power laws have not proved to be very useful expressions for the boundary on the operational space in JET with the ILW, confirming the preliminary indications reported in [42]. Consequently, the present work confirms the limitations of power laws for the investigation of thermonuclear plasmas, as already reported extensively for the energy confinement time and the radiation limit [46].…”

Section: Conclusion and Future Prospectssupporting

confidence: 85%

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Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools

et al. 2020

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The inadequacies of basic physics models for disruption prediction have induced the community to increasingly rely on data mining tools. In the last decade, it has been shown how machine learning predictors can achieve a much better performance than those obtained with manually identified thresholds or empirical descriptions of the plasma stability limits. The main criticisms of these techniques focus therefore on two different but interrelated issues: poor “physics fidelity” and limited interpretability. Insufficient “physics fidelity” refers to the fact that the mathematical models of most data mining tools do not reflect the physics of the underlying phenomena. Moreover, they implement a black box approach to learning, which results in very poor interpretability of their outputs. To overcome or at least mitigate these limitations, a general methodology has been devised and tested, with the objective of combining the predictive capability of machine learning tools with the expression of the operational boundary in terms of traditional equations more suited to understanding the underlying physics. The proposed approach relies on the application of machine learning classifiers (such as Support Vector Machines or Classification Trees) and Symbolic Regression via Genetic Programming directly to experimental databases. The results are very encouraging. The obtained equations of the boundary between the safe and disruptive regions of the operational space present almost the same performance as the machine learning classifiers, based on completely independent learning techniques. Moreover, these models possess significantly better predictive power than traditional representations, such as the Hugill or the beta limit. More importantly, they are realistic and intuitive mathematical formulas, which are well suited to supporting theoretical understanding and to benchmarking empirical models. They can also be deployed easily and efficiently in real-time feedback systems.

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Section: Conclusion and Future Prospectssupporting

confidence: 85%

“…In terms of future developments, one of the main priorities is certainly the applications of the developed methodology to support avoidance strategies [46,47], which will certainly require the refinement and use of new diagnostics particularly spectroscopy [45] and polarimetry [48].…”

Section: Conclusion and Future Prospectsmentioning

confidence: 99%

Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools

et al. 2020

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“…In the present paper, we have developed a general deep learning capability to train multiple models with distinct architectures within a single software suite that serves to promote adaptability to different temporal and spatial learning tasks and also to enable ensemble schemes for highly accurate prediction. Various machine learning based algorithms targeting different learning and prediction tasks have been independently validated for modern tokamaks such as DIII-D [4,14] and JET [23][24][25][26][27][28][29][30][31][32][33][34][35]. The capability of utilizing effective ensemble models in realtime plasma control systems is an important task for successful operation of future machines.…”

Section: Discussionmentioning

confidence: 99%

Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science

Dong

Felker

Svyatkovskiy

et al. 2021

J Mach Learn Model Comput

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We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma science. The onset of major disruptions is a critically important fusion energy science issue that must be resolved for advanced tokamak plasmas such as the $25B burning plasma international thermonuclear experimental reactor (ITER) experiment. While a variety of statistical methods have been used to address the problem of tokamak disruption prediction and control, recent approaches based on deep learning have proven particularly compelling. In the present paper, we introduce further improvements to the fusion recurrent neural network (FRNN) software suite, which delivered cross-machine disruption predictions with unprecedented accuracy using a large database of experimental signals from two major tokamaks. Up to now, FRNN was based on the long short-term memory (LSTM) variant of recurrent neural networks to leverage the temporal information in the data. Here, we implement and apply the "temporal convolutional neural network (TCN)" architecture to the time-dependent input signals. This allows highly optimized convolution operations to carry the majority of the computational load of training, thus enabling a reduction in training time, and the effective use of highperformance computing resources for hyperparameter tuning. At the same time, the TCN-based architecture achieves better predictive performance when compared with the LSTM architecture for various tasks for a representative fusion database.

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“…For example, most equilibrium reconstruction codes use the one-fluid approximation, assume that the resistivity is zero, that the plasma is in a steadystate toroidally symmetric regime, while bolometer tomography usually assumes that emissivity is toroidally symmetric (this assumption is required when cameras see the plasma in different toroidal positions). Unfortunately, these hypotheses are true as first approximations at best, while if one is interested in extracting more physically meaningful information, more detailed reconstruction should be performed [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

On the potential of physics-informed neural networks to solve inverse problems in tokamaks

Rossi,

Gelfusa,

Murari

2023

Nucl. Fusion

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Magnetic confinement nuclear fusion holds great promise as a source of clean and sustainable energy for the future. However, achieving net energy from fusion reactors requires a more profound understanding of the underlying physics and the development of efficient control strategies. Plasma diagnostics are vital to these efforts, but accessing local information often involves solving very ill-posed inverse problems. Regrettably, many of the current approaches for solving these problems rely on simplifying assumptions, sometimes inaccurate or not completely verified, with consequent imprecise outcomes. In order to overcome these challenges, the present study suggests employing Physics-Informed Neural Networks (PINNs) to tackle inverse problems in tokamaks. PINNs represent a type of neural network that is versatile and can offer several benefits over traditional methods, such as their capability of handling incomplete physics equations, of coping with noisy data, and of operating mesh-independently. In this work, PINNs are applied to three typical inverse problems in tokamak physics: equilibrium reconstruction, interferometer inversion, and bolometer tomography. The reconstructions are compared with measurements from other diagnostics and correlated phenomena, and the results clearly show that PINNs can be easily applied to these types of problems, delivering accurate results. Furthermore, we discuss the potential of PINNs as a powerful tool for integrated data analysis. Overall, this study demonstrates the great potential of PINNs for solving inverse problems in magnetic confinement thermonuclear fusion and highlights the benefits of using advanced machine learning techniques for the interpretation of various plasma diagnostics.

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Investigating the thermal stability of highly radiative discharges on JET with a new tomographic method

Cited by 17 publications

References 19 publications

Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools

Investigating the Physics of Tokamak Global Stability with Interpretable Machine Learning Tools

Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science

On the potential of physics-informed neural networks to solve inverse problems in tokamaks

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