{"title":"NTVTOK-ML: Fast surrogate model for neoclassical toroidal viscosity torque calculation in tokamaks based on machine learning methods","authors":"","doi":"10.1016/j.cpc.2024.109413","DOIUrl":null,"url":null,"abstract":"<div><div>The Neoclassical Toroidal Viscosity (NTV) torque is a crucial source of toroidal momentum in tokamaks, exerting significant influence on plasma instability and performance. Accurate numerical modeling of NTV torque is essential for experimental design and operation, as well as for gaining insight into the relevant physical processes. However, the time-consuming nature of NTV torque calculation poses challenges for its practical application in experiment analysis and physical investigations. In this study, we have developed NTVTOK-ML, a surrogate model for NTV torque calculation that combines the expressive power and fast inference of machine learning methods to achieve simultaneous accuracy and time efficiency. To obtain datasets for NTV torque, extensive numerical calculations using NTVTOK and MARS-F codes were performed under various plasma conditions of Experimental Advanced Superconducting Tokamak (EAST), covering a wide range of experimentally relevant parameter regimes and incorporating rich physical effects such as pitch angle scattering, full toroidal geometry, resonances, etc. For fixed magnetic perturbation case, NTVTOK-ML employs Multi-Layer Perceptron (MLP) deep neural network and eXtreme Gradient Boosting (XGBoost) ensemble learning techniques respectively. Furthermore, when considering linear plasma response effect, Convolutional Neural Network (CNN) is utilized to process two-dimensional magnetic perturbation data. The prediction accuracy of NTVTOK-ML is evaluated based on statistical metrics including coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>), mean squared error (<em>MSE</em>), and relative error; single sample prediction ability; and generalization ability - demonstrating its reliability in NTV torque prediction tasks. Importantly, the computational time required for predicting NTV torque using our proposed approach is significantly reduced compared to the original numerical code by several orders of magnitude. Additionally, the flexibility offered by the NTVTOK-ML framework allows users to optimize model performance under specific circumstances. Overall, our developed method provides an accessible solution for rapid yet accurate prediction of NTV torque while incorporating essential physical effects - thereby facilitating real-time or inter-shot analysis in experiments as well as comprehensive multi-scale nonlinear time evolution modeling.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> NTVTOK-ML</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/thcd9fbjd5.1</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> Apache-2.0</div><div><em>Programming language:</em> Python</div><div><em>Nature of problem:</em> The traditional numerical calculation of NTV torque in tokamaks is time-consuming, which hinders real-time or inter-shot experimental analysis and multi-scale nonlinear time evolution modeling. Simplifications to physical models have been employed to accelerate the calculations; however, they often result in quantitative or qualitative deviations within certain parameter regimes. Therefore, achieving both accuracy and high time efficiency simultaneously for NTV modeling is essential for experiment design and operation, as well as a comprehensive understanding of relevant physical processes.</div><div><em>Solution method:</em> 1. Generate a comprehensive plasma parameter space that encompasses a wide range of experimentally relevant tokamak plasma conditions; 2. Conduct numerous NTVTOK and MARS-F calculations to construct NTV torque datasets that incorporate various physical effects, such as pitch angle scattering, full toroidal geometry, and resonances; 3. Pre-process the datasets using dimensionless methods and logarithm transformation. For two-dimensional magnetic perturbation data, employ CNN for pre-processing; 4. Develop and train machine learning models based on deep neural networks or ensemble learning methods; 5. Evaluate the model performance in terms of statistical metrics, single sample prediction capability, generalization ability, and computational efficiency. The results indicate that the NTVTOK-ML surrogate model can be applied to diverse physical tasks in future studies.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Physics Communications","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010465524003369","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
The Neoclassical Toroidal Viscosity (NTV) torque is a crucial source of toroidal momentum in tokamaks, exerting significant influence on plasma instability and performance. Accurate numerical modeling of NTV torque is essential for experimental design and operation, as well as for gaining insight into the relevant physical processes. However, the time-consuming nature of NTV torque calculation poses challenges for its practical application in experiment analysis and physical investigations. In this study, we have developed NTVTOK-ML, a surrogate model for NTV torque calculation that combines the expressive power and fast inference of machine learning methods to achieve simultaneous accuracy and time efficiency. To obtain datasets for NTV torque, extensive numerical calculations using NTVTOK and MARS-F codes were performed under various plasma conditions of Experimental Advanced Superconducting Tokamak (EAST), covering a wide range of experimentally relevant parameter regimes and incorporating rich physical effects such as pitch angle scattering, full toroidal geometry, resonances, etc. For fixed magnetic perturbation case, NTVTOK-ML employs Multi-Layer Perceptron (MLP) deep neural network and eXtreme Gradient Boosting (XGBoost) ensemble learning techniques respectively. Furthermore, when considering linear plasma response effect, Convolutional Neural Network (CNN) is utilized to process two-dimensional magnetic perturbation data. The prediction accuracy of NTVTOK-ML is evaluated based on statistical metrics including coefficient of determination (), mean squared error (MSE), and relative error; single sample prediction ability; and generalization ability - demonstrating its reliability in NTV torque prediction tasks. Importantly, the computational time required for predicting NTV torque using our proposed approach is significantly reduced compared to the original numerical code by several orders of magnitude. Additionally, the flexibility offered by the NTVTOK-ML framework allows users to optimize model performance under specific circumstances. Overall, our developed method provides an accessible solution for rapid yet accurate prediction of NTV torque while incorporating essential physical effects - thereby facilitating real-time or inter-shot analysis in experiments as well as comprehensive multi-scale nonlinear time evolution modeling.
Program summary
Program Title: NTVTOK-ML
CPC Library link to program files:https://doi.org/10.17632/thcd9fbjd5.1
Licensing provisions: Apache-2.0
Programming language: Python
Nature of problem: The traditional numerical calculation of NTV torque in tokamaks is time-consuming, which hinders real-time or inter-shot experimental analysis and multi-scale nonlinear time evolution modeling. Simplifications to physical models have been employed to accelerate the calculations; however, they often result in quantitative or qualitative deviations within certain parameter regimes. Therefore, achieving both accuracy and high time efficiency simultaneously for NTV modeling is essential for experiment design and operation, as well as a comprehensive understanding of relevant physical processes.
Solution method: 1. Generate a comprehensive plasma parameter space that encompasses a wide range of experimentally relevant tokamak plasma conditions; 2. Conduct numerous NTVTOK and MARS-F calculations to construct NTV torque datasets that incorporate various physical effects, such as pitch angle scattering, full toroidal geometry, and resonances; 3. Pre-process the datasets using dimensionless methods and logarithm transformation. For two-dimensional magnetic perturbation data, employ CNN for pre-processing; 4. Develop and train machine learning models based on deep neural networks or ensemble learning methods; 5. Evaluate the model performance in terms of statistical metrics, single sample prediction capability, generalization ability, and computational efficiency. The results indicate that the NTVTOK-ML surrogate model can be applied to diverse physical tasks in future studies.
期刊介绍:
The focus of CPC is on contemporary computational methods and techniques and their implementation, the effectiveness of which will normally be evidenced by the author(s) within the context of a substantive problem in physics. Within this setting CPC publishes two types of paper.
Computer Programs in Physics (CPiP)
These papers describe significant computer programs to be archived in the CPC Program Library which is held in the Mendeley Data repository. The submitted software must be covered by an approved open source licence. Papers and associated computer programs that address a problem of contemporary interest in physics that cannot be solved by current software are particularly encouraged.
Computational Physics Papers (CP)
These are research papers in, but are not limited to, the following themes across computational physics and related disciplines.
mathematical and numerical methods and algorithms;
computational models including those associated with the design, control and analysis of experiments; and
algebraic computation.
Each will normally include software implementation and performance details. The software implementation should, ideally, be available via GitHub, Zenodo or an institutional repository.In addition, research papers on the impact of advanced computer architecture and special purpose computers on computing in the physical sciences and software topics related to, and of importance in, the physical sciences may be considered.