Citrin, J.

Ho, A.

English

Digital twinning of a tokamak device requires fast system state inference. Physics-based computational models that predict future states are often too slow to be actionable, and thus undesirable for offline scenario planning. These tasks may be performed faster if the physics-based model is replaced by a neural network-based surrogate. Obtaining the labels to train the surrogate can be computationally expensive, additionally, some inputs may result in trivial outputs. Here we propose a two-stage active learning pipeline for digital twinning of gyrokinetic turbulence in the core of tokamak fusion plasmas. Our pipeline leverages an uncertainty-based acquisition function which greatly outperforms random acquisition and leads to a reduction of 99.6% in the amount of input-output mappings needed from the physical model without compromising on performance.</p

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 An active learning pipeline for surrogate models of gyrokineticturbulenceCitation for published version (APA):JET Contributors, Ho, A., & Citrin, J. (2022). An active learning pipeline for surrogate models of gyrokineticturbulence. In 48th EPS Conference on Plasma Physics 27 June - 1 July 2022 (Europhysics conferenceabstracts; Vol. 46A). European Physical Society (EPS). http://ocs.ciemat.es/EPS2022PAP/pdf/P5b.119.pdfDocument status and date:Published: 01/01/2022Document Version:Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)Please check the document version of this publication:• A submitted manuscript is the version of the article upon submission and before peer-review. There can beimportant differences between the submitted version and the official published version of record. Peopleinterested in the research are advised to contact the author for the final version of the publication, or visit theDOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and pagenumbers.Link to publicationGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, pleasefollow below link for the End User Agreement:www.tue.nl/taverneTake down policyIf you believe that this document breaches copyright please contact us at:openaccess@tue.nlproviding details and we will investigate your claim.Download date: 05. Oct. 2023An active learning pipeline for surrogate models of gyrokinetic turbulence.J. Barr1, T. Madula1, L. Zanisi2, A. Ho3,4, J. Citrin3,4, V. Gopakumar2, and JET contributors *1 UCL’s Centre for Doctoral Training in Data Intensive Sciences, University College London; 2 Culham Centre forFusion Energy, Abingdon, ; 3 Dutch Institute for Fundamental Energy Research, Eindhoven, Netherlands; 4Scienceand Technology of Nuclear Fusion Group, Eindhoven University of Technology, Eindhoven, NetherlandsAbstractDigital twinning of a tokamak device requires fast system state inference. Physics-basedcomputational models that predict future states are often too slow to be actionable, and thusundesirable for offline scenario planning. These tasks may be performed faster if the physics-based model is replaced by a neural network-based surrogate. Obtaining the labels to train thesurrogate can be computationally expensive, additionally, some inputs may result in trivial out-puts. Here we propose a two-stage active learning pipeline for digital twinning of gyrokineticturbulence in the core of tokamak fusion plasmas. Our pipeline leverages an uncertainty-basedacquisition function which greatly outperforms random acquisition and leads to a reductionof 99.6% in the amount of input-output mappings needed from the physical model withoutcompromising on performance.IntroductionTurbulent transport in Tokamak plasmas is a major roadblock to achieving fusion power [1]. Itis thus crucial to forecast expected turbulent fluxes in the operating parameter space of a giventokamak for design, validation and optimization at the pre- and post-experiment stage. In general,a general-purpose surrogate would be expensive to obtain, as the computational effort needed toobtain the labels from all potentially needed realisations of the physical model would be pro-hibitively long. Instead, a surrogate can be trained directly on experimental inputs, which bettercapture the parameter subspace of the machine. The surrogate can then be re-evaluated and up-dated when new data becomes available.Experimental data from a given machine may be redundant, and data labelling (i.e. obtainingthe input-output mapping from the physical model) for similar inputs would be inefficient. More-over, by the nature of the critical threshold characteristic of tokamak turbulence and measurementuncertainties, not all input plasma states result in unstable modes. Thus, a significant proportionof the computational budget to obtain the turbulent fluxes is spent on stable inputs, which can bewasteful. Active learning may be used to select the inputs that would be most useful to update thesurrogate.In this paper, we use active learning to build a data-efficient surrogate of plasma core turbulenttransport based on the QuaLiKiz model [2, 3] and a dataset of JET shots for which QuaLikiz runs*See the author list of ‘Overview of JET results for optimising ITER operation’ by J. Mailloux et al.48th EPS Conference on Plasma Physics P5b.119have been obtained in [4]. The objective of Active Learning [5] is to efficiently allocate resourcesby only acquiring the labels that would be most beneficial to obtain. We propose a two-stagelearning paradigm that exploits the binary nature of plasma core instabilities. Our framework usesa small initially labelled dataset to pre-train a stability classifier, and a regressor to predict therelated turbulent fluxes. The initial dataset is then augmented by acquiring the labels of candidateinputs that are classified as likely to result in a growing instability, and that would most improvethe range of validity of the surrogate. Our framework allows us to achieve the performance of asurrogate trained on the full-dataset but with only 0.4% of the data.DataWe use an experimentally-based dataset from the JET (Joint European Torus) tokamak, whichcontains the turbulent transport calculation inputs and related QuaLiKiz outputs needed to makea surrogate model. This dataset was originally used in [4] to train neural network ensembles,without active learning. Here we focus on the Ion Temperature Gradient turbulence [6], for whichonly approximately 25% of inputs will develop turbulent transport, for a total of 4,000,000 pointsavailable. Specifically, we predict the heat flux of ions, qi,IT G.Active LearningGiven an unlabelled pool of experimental inputs, a classifier and a regressor are pretrained on asmall random sample of data whose labels were obtained by running QuaLiKiz in [4]. Hereafter,the networks and the labelled dataset are updated following a two-stage active learning pipelineshown in Figure 1. The classifier is tasked with screening a sample of candidate points of sizeCandidateSize from the unlabelled pool. CandidateSize is chosen to be 10,000, which reflectsthe number of distinct inputs obtained from a single JET plasma discharge. An acquisition functionselects which of the candidates should be labelled, and those inputs are then appended to thetraining data, to retrain the regressor. Specifically, we select TopUncertain% (set to 25%) of theinputs with the highest regressor variance. The points that were wrongly classified as unstable arestored in a buffer of size ClassifierBuffer (set to 200) which, when filled, is used to retrain theclassifier along with the previous training data. Both the regressor and the classifiers are equippedwith Dropout layers [7]. We use Monte Carlo Dropout [8] to estimate the uncertainty of the NNregressor. Although the classifier achieved an actionable performance in the pre-trained phase,more advanced acquisition functions based on both the regressor and classifier uncertainties willneed to be explored.The architecture for the classifier and regressor consists of a simple feed-forward neural networkconsisting of an input size of 15 and 5 hidden layers with sizes [128, 256, 512, 256, 128]. Adropout rate of 0.1 was employed in each layer along with ReLU activation functions. We usedthe Adam optimiser with a learning rate of 0.001, and a weight decay of 0.0001. The holdout setconsists on 50,000 points.48th EPS Conference on Plasma Physics P5b.119Figure 1: Schematic diagram of active learning framework. Each continual learning task consistsof multiple iterations of the steps in the blue box.ResultsWe conducted various experiments to investigate the effectiveness of an uncertainty-based ac-quisition function and the effect of the stability classifier on the pipeline performance. An initialtraining dataset size of 5,000 was used. Classifier performance across iterations was observed tobe relatively unchanged, achieving a constant F1 score of 87%. We evaluate the performance ofthe Active Learning pipeline in terms of the Mean Squared Error (MSE) between the predictedand true fluxes on a holdout set,MSE =1NN∑j(q j,true −q j,pred)2 (1)As shown in Figure 2, uncertainty-based sampling provides significantly improved performancecompared to random sampling. Figure 2 shows that the combination of the classifier and the useof uncertainty-based acquisition provides a much greater decrease in MSE than using either indi-vidually. As only 25% of inputs result in a growing turbulence, the classifier stage of the pipelineprovides data-efficiency.Table 1 summarises the final test loss after 25 pipeline iterations. The combined classifier anduncertainty acquisition model achieves comparable performance to a model trained on the fulldataset, but with only 0.4% of data. We conducted experiments using different initial training setsizes that are then evaluated at the same final training set size. It was found that using 5,000 pointsto pretrain the networks had a better final performance than when starting with 1,000 points, butfurther increases in the initial training set size provide increasingly marginal gains.We note that the MSE is correlated with, but not necessarily fully reflective of, the surrogatequality in the final tokamak modelling application. Subtle features of the input-output mappingmust also be captured, as shown in [9]. Future work will include more domain-specific metrics.48th EPS Conference on Plasma Physics P5b.119Figure 2: Test losses for qi,IT G[GB] using different acquisition methods. Bottom plots show thenumber of points sampled from the unlabelled pool at each iteration. An initial training dataset of5,000 points was used. The lines and shaded areas are the means and one standard deviation of 10random realisations.Data Acquisition Method qi,IT G[GB] MSENo Classifier - Uncertainty 52.9±3.2Classifier - Random 48.5±2.0Classifier - Uncertainty 37. ± 1.6Full Dataset 38.6±0.5Table 1: The average test MSE and one standard deviation after 25 active learning pipeline itera-tions trained on the heat flux of ion temperature gradient turbulence in different experiments.ConclusionThis paper investigates the use of a two-stage active learning pipeline for the modelling ofturbulent transport in tokamak reactors. The inclusion of a classifier stage to identify regions ofthe input space that lead to growing turbulence modes improves the surrogate model performanceand is more data efficient. Additionally, when used in combination with an uncertainty-basedacquisition function, the performance of the pipeline matches that of full-dataset training but withonly 0.4% of the data, which would have resulted in a significant reduction in computational timespent generating QuaLiKiz input-output mappings.AcknowledgementsWe would like to thank S. Pamela, B. Joachimi and A. Spurio Mancini for useful comments.References[1] EJ Doyle et al. In: Nuclear Fusion 47.6 (2007), S18. [2] C Bourdelle et al. In: Plasma Physics and ControlledFusion 58.1 (2015), p. 014036. [3] J. Citrin et al. In: Plasma Physics and Controlled Fusion 59.12 (2017), p. 124005.[4] A. Ho et al. In: Physics of Plasmas 28 (2021), p. 032305. [5] Pengzhen Ren et al. In: CoRR abs/2009.00236(2020). arXiv: 2009.00236. URL: https://arxiv.org/abs/2009.00236. [6] W Horton. In: Reviews of ModernPhysics 71.3 (1999), p. 735. [7] Nitish Srivastava et al. In: J. Mach. Learn. Res. 15 (2014), pp. 1929–1958. [8] YarinGal and Zoubin Ghahramani. In: international conference on machine learning. PMLR. 2016, pp. 1050–1059. [9]Karel Lucas van de Plassche et al. In: Physics of Plasmas 27.2 (2020), p. 022310.48th EPS Conference on Plasma Physics P5b.119

An active learning pipeline for surrogate models of gyrokinetic turbulence

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An active learning pipeline for surrogate models of gyrokinetic turbulence

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