{"title":"基于 Grad-Shafranov 解决方案的深度学习方法,从多个托卡马克的安全系数中恢复等离子体电流密度曲线","authors":"Hanyu Zhang, Lina Zhou, Yueqiang Liu, Guangzhou Hao, Shuo Wang, Xu Yang, Yutian Miao, Ping Duan, Long Chen","doi":"10.1088/2058-6272/ad13e3","DOIUrl":null,"url":null,"abstract":"\n Many magnetohydrodynamic stability analysis requires generation of a set of equilibria with fixed safety factor q-profile while varying other plasma parameters. A neural network (NN)-based approach is investigated that facilitates such a process. Both the multi-layer perceptron (MLP) based NN and the 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, the initial CNN with one convolutional layer is found to perform better than the initial MLP model. In particular, the 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- or J-profile across multiple tokamak devices. As an important application, these NN-based equilibrium profile convertors can be utilized to provide good initial guess for iterative equilibrium solvers, where the desired input quantity is the safety factor instead of the plasma current density.","PeriodicalId":20250,"journal":{"name":"Plasma Science & Technology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning approaches to recover plasma current density profile from safety factor based on Grad-Shafranov solutions across multiple tokamaks\",\"authors\":\"Hanyu Zhang, Lina Zhou, Yueqiang Liu, Guangzhou Hao, Shuo Wang, Xu Yang, Yutian Miao, Ping Duan, Long Chen\",\"doi\":\"10.1088/2058-6272/ad13e3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Many magnetohydrodynamic stability analysis requires generation of a set of equilibria with fixed safety factor q-profile while varying other plasma parameters. A neural network (NN)-based approach is investigated that facilitates such a process. Both the multi-layer perceptron (MLP) based NN and the 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, the initial CNN with one convolutional layer is found to perform better than the initial MLP model. In particular, the 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- or J-profile across multiple tokamak devices. As an important application, these NN-based equilibrium profile convertors can be utilized to provide good initial guess for iterative equilibrium solvers, where the desired input quantity is the safety factor instead of the plasma current density.\",\"PeriodicalId\":20250,\"journal\":{\"name\":\"Plasma Science & Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Plasma Science & Technology\",\"FirstCategoryId\":\"1089\",\"ListUrlMain\":\"https://doi.org/10.1088/2058-6272/ad13e3\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"PHYSICS, FLUIDS & PLASMAS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Plasma Science & Technology","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.1088/2058-6272/ad13e3","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHYSICS, FLUIDS & PLASMAS","Score":null,"Total":0}
Deep learning approaches to recover plasma current density profile from safety factor based on Grad-Shafranov solutions across multiple tokamaks
Many magnetohydrodynamic stability analysis requires generation of a set of equilibria with fixed safety factor q-profile while varying other plasma parameters. A neural network (NN)-based approach is investigated that facilitates such a process. Both the multi-layer perceptron (MLP) based NN and the 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, the initial CNN with one convolutional layer is found to perform better than the initial MLP model. In particular, the 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- or J-profile across multiple tokamak devices. As an important application, these NN-based equilibrium profile convertors can be utilized to provide good initial guess for iterative equilibrium solvers, where the desired input quantity is the safety factor instead of the plasma current density.
期刊介绍:
PST assists in advancing plasma science and technology by reporting important, novel, helpful and thought-provoking progress in this strongly multidisciplinary and interdisciplinary field, in a timely manner.
A Publication of the Institute of Plasma Physics, Chinese Academy of Sciences and the Chinese Society of Theoretical and Applied Mechanics.