Disruption prediction with artificial intelligence techniques in tokamak plasmas

Mailloux, J.; Abid, N.; Abraham, K.; Abreu, P.; Adabonyan, O.; Adrich, P.; Afanasev, V.; Afzal, Muhammad; Ahlgren, T.; Aho-Mantila, L.; Aiba, N.; Airila, Markus; Akhtar, M.; Albanese, R.; Alderson-Martin, M.; Alegre, D.; Aleiferis, S.; Aleksa, A.; Alekseev, A. G.; Alessi, E.; Aleynikov, P.; Algualcil, J.; Ali, M.; Allinson, M.; Alper, B.; Alves, E.; Ambrosino, G.; Ambrosino, R.; Amosov, V. N.; Sundén, E. Andersson; Andrew, P.; Angelini, B. M.; Angioni, C.; Antoniou, I.; Appel, L.C.; Appelbee, C.; Aria, S.; Ariola, M.; Artaserse, G.; Arter, W.; Artigues, V.; Asakura, N.; Ash, A.; Ashikawa, N.; Aslanyan, V.; Astrain, M.; Asztalos, O.; Auld, D.; Auriemma, F.; Austin, Y.; Avotiņa, Lı̅ga; Aymerich, E.; Baciero, A.; F., Bairaktaris,; Balbin, J.; Balbinot, L.; Balboa, I.; Balden, M.; Balshaw, C.; Balshaw, N.; Bandaru, V.; Banks, J.; Baranov, Y.; Barcellona, C.; Barnard, A.; Barnard, M.; Barnsley, R.; Barth, A.; Baruzzo, M.; Barwell, S.; Bassan, M.; Batista, Antônio M.; Batistoni, P.; Baumane, L.; Bauvir, Bertrand; Baylor, L.R.; Beaumont, P.; Beckett, D.; Begolli, A.; Beidler, Matthew; Bekris, N.; Beldishevski, M.; Belli, E. A.; Belli, F.; Belonohy, É.; Yaala, M. Ben; Benayas, J.; Bentley, J.; Bergsåker, Henric; Bernardo, J.; Bernert, M.; Berry, Mike; Bertalot, L.; Betar, H.; Beurskens, M.; Bickerton, S.; Bieg, B.; Bielecki, J.; Bierwage, Andreas; Biewer, T. M.; Bilato, R.; Bílková, P.; Birkenmeier, G.; Bishop, H.; Bizarro, João P. S.; Blackburn, Jeffrey L.; Blanchard, P.; Blatchford, Peter; Bobkov, V.; Boboc, A.; Bohm, P.; Bohm, T.; Bolshakova, І.; Bolzonella, T.; Bonanomi, N.; Bonfiglio, D.; Bonnin, X.; Bonofiglo, P.; Boocock, S.; Booth, A.; Booth, J.; Borba, D.; Borodin, D.; Borodkina, I.; Boulbe, Cédric; Bourdelle, C.; Bowden, Mark; Boyd, K.; Mihalić, Iva Božičević; Bradnam, S. C.; Braic, V.; Brandt, L.; Bravanec, R.; Breǐzman, B. N.; Brett, A.; Brezinsek, S.; Brix, M.; Bromley, K.; Brown, B.; Brunetti, D.; Buckingham, R.; Buckley, Margaret; Budny, R.; Buermans, J.; Bufferand, Hugo; Buratti, P.; Burgess, A.; Buscarino, Arturo; Busse, Angela; Butcher, D.; Cal, E. de la; Calabrò, G.; Calacci, L.; Calado, R.; Camenen, Y.; Canal, G.; Cannas, B.; Cappelli, M.; Carcangiu, Sara; Card, P.; Cardinali, A.; Carman, P.; Carnevale, D.; Carr, M.; Carralero, D.; Carraro, L.; Carvalho, I.; Carvalho, P.; Casiraghi, I.; Casson, F. J.; Castaldo, C.; Catalan, J. P.; Catarino, N.; Causa, F.; Cavedon, M.; Cecconello, M.; Challis, C.; Chamberlain, B.; Chang, C.-S.; Chankin, A.; Chapman, B.; Chernyshova, M.; Chiariello, A.; Chmielewski, P.; Chomiczewska, A.; Chone, L.; Ciraolo, Guido; Ćirić, D.; Citrin, J.; Ciupinski, t.; Clark, M.W.; Clarkson, R. B.; Clements, Christopher F.; Cleverly, M.; Coad, J. P.; Coates, P.; Cobalt, A.; Coccorese, V.; Coelho, R.; Coenen, J. W.; Coffey, I.; Colangeli, A.; Colas, L.; C., Collins,; J., Collins,; Collins, S.; Conka, D.; Conroy, S.; Conway, B.; Conway, N. J.; Coombs, D.; Cooper, P.; Cooper, S.; Corradino, Claudia; Corrigan, G.; Coster, D.; Cox, P.; Craciunescu, T.; Cramp, S.; Crapper, C.; Craven, D.; Craven, R.; Esposito, Marco Crialesi; Croci, G.; Croft, D.; Croitoru, A.; Crombé, Kristel; Cronin, T.; Cruz, N.; Crystal, C.; Cseh, G.; Čufar, Aljaž; Cullen, A.L.; Curuia, M.; Czarski, T.; Dabirikhah, H.; Molin, A. Dal; Dale, E.; Dalgliesh, P.; Dalley, S.; Dankowski, J.; David, P.; Davies, A.; Davies, S.; Davis, G.; Dawson, K.; Dawson, S.; Day, I.E.; Bock, Maarten De; Temmerman, G. De; Tommasi, G. De; Deakin, K.; Deane, Jonathan H. B.; Dejarnac, R.; Sarto, D. Del; Delabie, E.; del‐Castillo‐Negrete, Diego; Dempsey, A.; Dendy, R. O.; Devynck, P.; Siena, A. Di; Troia, C. Di; Dickson, T.; Dincă, P.; Dittmar, T.; Dobrashian, J.; Doerner, R.; Donné, A. J. H.; Dorling, S.; Dormido-Canto, S.; Douai, D.; S., Dowson,; Doyle, R.; Dreval, M.; Drewelow, P.; Drews, P.; Drummond, G.; Duckworth, Ph.; Dudding, H.; Dümont, R.; Dumortier, P.; Dunai, D.; Dunatov, T.; Dunne, M.; Ďuran, I.; Durodié, F.; Dux, R.; Dvornova, A.; Eastham, R.; Edwards, J.; Eich, T.; Eichorn, A.; Eidietis, N. W.; Eksaeva, A.; Haroun, H. El; Ellwood, G.; Elsmore, C.; Embréus, Ola; S., Emery,; Ericsson, Göran; Eriksson, B.; Eriksson, F.; Eriksson, J.; Eriksson, L.‐G.; Ertmer, S.; Esquembri, S.; Esquisabel, A. 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Zarzoso; Zastrow, K.‐D.; Zayachuk, Y.; Zerbini, M.; Zhang, W.; Zhou, Y.; Zlobinski, M.; Zocco, A.; Zohar, A.; Zoiţa, V.; Zoletnik, S.; Zotta, V. K.; Zoulias, I.; Zwingmann, W.; Zychor, I.

doi:10.1038/s41567-022-01602-2

Cited by 41 publications

(45 citation statements)

References 54 publications

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“…Three were indeed installed in JET tokamak real time network and one, called APODIS, was also used in feedback loop [7]. For a review on machine learning methods for disruption prediction the reader is referred to [8]. The main drawback of this first generation of predictors resided in their requirements of examples for the training.…”

Section: When the Loss Of Stability Is Fatal For Complex Systems: Dis...mentioning

confidence: 99%

Detection of changes in the dynamics of thermonuclear plasmas to improve the prediction of disruptions

Murari¹

2022

Nonlinear Dyn

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In particular circumstances, nonlinear systems can collapse suddenly and abruptly. Anomalous detection is therefore an important task. Unfortunately, many phenomena occurring in complex systems out of equilibrium, such as disruptions in tokamak thermonuclear plasmas, cannot be modelled from first principles in real time compatible form and therefore data driven, machine learning techniques are often deployed. A typical issue, for training these tools, is the choice of the most adequate examples. Determining the intervals, in which the anomalous behaviours manifest themselves, is consequently a challenging but essential objective. In this paper a series of methods are deployed to determine when the plasma dynamics of the tokamak configuration varies, indicating the onset of drifts towards a form of collapse called disruption. The techniques rely on changes in various quantities derived from the time series of the main signals: from the embedding dimensions to the pr 1 operties of recurrence plots and to indicators of transition to chaotic dynamics. The methods, being mathematically completely independent, provide quite robust indications about the intervals, in which the various signals manifest a predisruptive behaviour. Consequently, the signal samples, to be used for supervised machine learning predictors, can be defined precisely, on the basis of the plasma dynamics. This information can improve significantly not only the performance of machine learning classifiers but also the physical understanding of the phenomenon.

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Section: When the Loss Of Stability Is Fatal For Complex Systems: Dis...mentioning

confidence: 99%

Detection of changes in the dynamics of thermonuclear plasmas to improve the prediction of disruptions

Murari¹

2022

Nonlinear Dyn

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show abstract

“…In terms of artificial intelligence technologies, practically all the main machine learning threads have been tried to predict disruptions: artificial neural networks, support vector machines, fuzzy logic, generative topographic mapping, deep learning and even reinforcement learning [17]. With regard to real-time signal processing, the methods implemented have explored practically all known data analysis techniques for time series in the time domain, in the frequency domain, and in the combined time/frequency domains [18,19].…”

Section: A Data-driven Physics-based Approach To Prediction For Proxi...mentioning

confidence: 99%

A hybrid physics/data-driven logic to detect, classify, and predict anomalies and disruptions in tokamak plasmas

Rossi,

Gelfusa,

Craciunescu

et al. 2024

Nucl. Fusion

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Disruptions are abrupt collapses of the configuration that have afflicted all tokamaks ever operated. Reliable observers are a prerequisite to the definition and the deployment of any realistic strategy of countermeasures to avoid or mitigate disruptions. Lacking first principle models of the dynamics leading to disruptions, in the past decades empirical predictors have been extensively studied and some were even installed in JET real-time network. Having been conceived as engineering tools, they were often very abstract. In this work, physics and data-driven methodologies are combined to identify the main macroscopic precursors of disruptions: magnetic instabilities, abnormal kinetic profiles and radiation patterns. Machine learning predictors utilising these observers can not only detect and classify these anomalies but also determine their probability of occurrence and estimate the time remaining before their onset. These tools have been applied to a database of about two thousand JET discharges with various isotopic compositions including DT, in conditions simulating in all respects real time deployment. Their performance would meet ITER requirements, and they are expected to be easily transferrable to larger devices, because they rely only on normalised quantities, form factors, and physical/empirical scaling laws.

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“…Since more than a decade, as a consequence of the lack of full disruption models from first principles [1,2], the recognition of disruptive events has been carried out by means of machine learning techniques [3]. Typically, supervised classification methods based on two classes of examples (disruptive and non-disruptive) have been developed.…”

Section: Prediction For Mitigation When Missing Important Diagnostic ...mentioning

confidence: 99%

Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

Vega,

Dormido-Canto,

Castro

et al. 2024

Nucl. Fusion

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This article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks. The objective consists of making predictions with real-time signals during the discharges (as usual) but after training the predictor also with any kind of data at training time that is not available during discharge execution. The latter kind of data is known as privileged information. Taking into account the limited number of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA, a predictor with a line integrated density signal and the mode lock signal as privileged information has been developed and tested with 1437 JET discharges. The success rate with positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the methodology, exemplified with data relevant to the beginning of JT-60SA operation, is absolutely general and can be applied to any combination of diagnostic signals

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Disruption prediction with artificial intelligence techniques in tokamak plasmas

Cited by 41 publications

References 54 publications

Detection of changes in the dynamics of thermonuclear plasmas to improve the prediction of disruptions

Detection of changes in the dynamics of thermonuclear plasmas to improve the prediction of disruptions

A hybrid physics/data-driven logic to detect, classify, and predict anomalies and disruptions in tokamak plasmas

Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

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