Hyper-dimensional Time-series Data Analysis With Reservoir .

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Hyper-dimensional time-series data analysis with reservoircomputing networks to predict plasma profiles in tokamakAzarakhsh (Aza) Jalalvand1Data scientistVisiting postdoc at Plasma Control Group (Princeton University)Co-authors: J. Abbate2,3, R. Conlin2,3, E. Kolemen2,3, G. Verdoolaege1Collaboration: DIII-D Team1 GhentUniversity (Belgium)2 PrincetonUniversity3 PrincetonPlasma Physics LaboratoryPresented at the1APS-DPP meetingNovember 2020A. Jalalvand / Nov. 2020 / APS-DPP

Outline2 What is tokamak? why data-driven models for plasma condition monitoring? What is Reservoir Computing Network? Real-time adaptive RCN models for plasma profile predictionA. Jalalvand / Nov. 2020 / APS-DPP

Plasma condition monitoring in TokamaksaExample Wall Damagefrom JETMatthews et al. Physica Scripta, T167, 2016ITER and future reactorsneed disruption avoidance solutions3A. Jalalvand / Nov. 2020 / APS-DPP

Data-Driven Transport Model for DIII-DGiven state (and actuators), can we predict how plasma will evolve on transport timescales( 100-200ms)?x(t) x(t ฮ”t) x(t) f[x(t), u(t)]Electron temperature (Te)Electron density (ne)Safety factor (q)Rotation (๐›บ)Pressure (P)f[x(t), u(t)]u(t) 4PinjectedTinjectedIpโŒฉneโŒช target Te ne q ฮฉ P4A. Jalalvand / Nov. 2020 / APS-DPP

* Jaeger H. (2001) The "echo state" approach toanalyzing and training recurrent neural networks.GMD Report 148, GMD - German NationalResearch Institute for Computer ScienceWhat is Reservoir computing network*? Reservoir computing network (RCN)โ€“โ€“โ€“โ€“โ€“Recurrent neural networkRandomly connected early layers.Only Last layer is trained using linear regression.Suitable for time-series data analysisMuch faster and easier training procedurecompared to DNNs.๐‘Š๐‘–๐‘–๐‘›The model can be adapted to thenew data in real-time!Adaptation takes only 100ms on alaptop CPU!5๐‘ˆ๐‘ก ๐‘…๐‘ก 1๐‘Š๐‘–๐‘Ÿ๐‘’๐‘A. Jalalvand / Nov. 2020 / APS-DPP๐œ†1 ๐œ†๐‘ 15

How does RCN work?Lin. Reg.6A. Jalalvand / Nov. 2020 / APS-DPPScore6/10

How does RCN work?Lin. Reg.๐‘–๐‘›๐‘Š1 2 0.823 0.124๐‘Ÿ๐‘’๐‘๐‘Š2 2 0.341.04 1.050.28Win and Wrec are set randomly7A. Jalalvand / Nov. 2020 / APS-DPPScore6/10

How does RCN work?Lin. Reg.๐‘–๐‘›๐‘Š1 2 0.823 0.124๐‘Ÿ๐‘’๐‘๐‘Š2 2 0.341.04Score6/10 1.050.28tanh(๐‘ฅ)Lin. Reg.Score8/10RCN randomly projects the inputs to a (much) larger non-linear feature space.8A. Jalalvand / Nov. 2020 / APS-DPP

Adaptive Data-driven Profile Prediction ModelsCNN/LSTMTrainingPerformanceTrain a on thedata from2013-20185h on GPUSOTAPredict theprofiles for2019Predict theprofiles forthe same period9A. Jalalvand / Nov. 2020 / APS-DPP

Adaptive Data-driven Profile Prediction ModelsBy means of Reservoir Computing NetworkTrainingPerformance10CNN/LSTMRCN5h on GPU45s on CPUSOTAClose to DNNA. Jalalvand / Nov. 2020 / APS-DPP

Adaptive Data-driven Profile Prediction ModelsBy means of Reservoir Computing Network *TrainingPerformanceAdaptation11CNN/LSTMRCN5h on GPU45s on CPUSOTAClose to DNNDifficultEasy (100ms)A. Jalalvand / Nov. 2020 / APS-DPP* A. Jalalvand, J. Abbate, R. Conlin, G. Verdoolaege,E. Kolemen,"Real-Time and Adaptive Reservoir Computing with anApplication to Profile Prediction in Fusion Plasma",IEEE Trans. on Neural Net. & Learning Systems, 2020.Real-time Adaptingevery 500ms

Conclusion RCN performs similar to state-pf-the-art DNN models in predictingplasma profiles. Easy and fast training procedure of RCN is an important asset forquick adaptation to the new environment.12A. Jalalvand / Nov. 2020 / APS-DPP

Thank you!13A. Jalalvand / Nov. 2020 / APS-DPP

3 A. Jalalvand / Nov. 2020 / APS-DPP a Plasma condition monitoring in Tokamaks Example Wall Damage from JET Matthews et al. Physica Scripta, T167, 2016 ITE

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