首页|Deep learning approaches to recover the plasma current density profile from the safety factor based on Grad-Shafranov solutions across multiple tokamaks
Deep learning approaches to recover the plasma current density profile from the safety factor based on Grad-Shafranov solutions across multiple tokamaks
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Many magnetohydrodynamic stability analyses require generation of a set of equilibria with a fixed safety factor q-profile while varying other plasma parameters.A neural network(NN)-based approach is investigated that facilitates such a process.Both multilayer perceptron(MLP)-based NN and convolutional neural network(CNN)models are trained to map the q-profile to the plasma current density J-profile,and vice versa,while satisfying the Grad-Shafranov radial force balance constraint.When the initial target models are trained,using a database of semi-analytically constructed numerical equilibria,an initial CNN with one convolutional layer is found to perform better than an initial MLP model.In particular,a trained initial CNN model can also predict the q-or J-profile for experimental tokamak equilibria.The performance of both initial target models is further improved by fine-tuning the training database,i.e.by adding realistic experimental equilibria with Gaussian noise.The fine-tuned target models,referred to as fine-tuned MLP and fine-tuned CNN,well reproduce the target q-orJ-profile across multiple tokamak devices.As an important application,these NN-based equilibrium profile convertors can be utilized to provide a good initial guess for iterative equilibrium solvers,where the desired input quantity is the safety factor instead of the plasma current density.
plasma equilibriumdeep learningsafety factor profilecurrent density profiletokamak
张瀚予、周利娜、刘钺强、郝广周、王硕、杨旭、苗雨田、段萍、陈龙
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College of Science,Dalian Maritime University,Dalian 116026,People's Republic of China
General Atomics,San Diego 92186-5608,United States of America
Southwestern Institute of Physics,Chengdu 610041,People's Republic of China
Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology,Chongqing Technology and Business University,Chongqing 400067,People's Republic of China
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国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金Dalian Youth Science and Technology Project中央高校基本科研业务费专项Young Scientists Fund of the Natural Science Foundation of Sichuan Province
NETL
NSTL
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