arXiv subjects

Chenguang Wan

Publications and source records attributed to Chenguang Wan.

11 recordsLinked to original sources

arXiv2026

Plasma GraphRAG: Physics-Grounded Parameter Selection for Gyrokinetic Simulations

Accurate parameter selection is fundamental to gyrokinetic plasma simulations, yet current practices rely heavily on manual literature reviews, leading to inefficiencies and inconsistencies. We introduce Plasma GraphRAG, a novel framework that integrates Graph Retrieval-Augmented Generation (GraphRAG) with large language models (LLMs) for automated, physics-grounded parameter range identification. By constructing a domain-specific knowledge graph from curated plasma literature and enabling structured retrieval over graph-anchored entities and relations, Plasma GraphRAG enables LLMs to generate accurate, context-aware recommendations. Extensive evaluations across five metrics, comprehensiveness, diversity, grounding, hallucination, and empowerment, demonstrate that Plasma GraphRAG outperforms vanilla RAG by over $10\%$ in overall quality and reduces hallucination rates by up to $25\%$. {Beyond enhancing simulation reliability, Plasma GraphRAG offers a methodology for accelerating scientific discovery across complex, data-rich domains.

physics.plasm-ph↗

arXiv2026

Machine learning prediction of plasma behavior from discharge configurations on WEST

Accurately predicting plasma behavior based on discharge configurations is essential for the safe and efficient operation of tokamak experiments. While physics-based integrated modeling codes provide valuable insights, their high computational cost limits their applicability for fast scenario design and control optimization. In this study, we propose a transformer-based machine learning model to predict key global plasma parameters on the WEST tokamak, including the normalized beta ($\beta_{n}$), toroidal beta ($\beta_{t}$), poloidal beta ($\beta_{p}$), plasma stored energy ($W_{\mathrm{mhd}}$), safety factor at the magnetic axis ($q_{0}$), and safety factor at the 95% flux surface ($q_{95}$). The model uses only signals that can be defined before the discharge, such as magnetic coil currents, auxiliary heating power, plasma current reference, and line-averaged plasma density. Trained on 550 discharges from the WEST campaigns, the model demonstrates an average mean square error (MSE) loss of 0.026, an average coefficient of determination $R^{2}$ of 0.94, and achieves inference times on the order of 0.1 seconds. These results highlight the potential of data-driven surrogate models for assisting in discharge planning, scenario evaluation, and real-time control of tokamak plasmas.

physics.plasm-ph↗

arXiv2025

Reconstructing High-fidelity Plasma Turbulence with Data-driven Tuning of Diffusion in Low Resolution Grids

Developing physically consistent closure models is a longstanding challenge in simulating plasma turbulence, even in minimal systems such as the two-field Hasegawa-Wakatani (HW) model, which captures essential features of drift-wave turbulence with a reduced set of variables. In this work, we leverage theoretical insights from Direct Interaction Approximation (DIA) to construct a six-term closure structure that captures the dominant turbulent transport processes, including both diffusion and hyper-diffusion. While the mathematical form of the closure is fully prescribed by DIA, the corresponding transport coefficients are learned from data using physics-informed neural networks (PINNs). The resulting Extended HW model with Closure (EHW-C) model reveals several nontrivial features of plasma turbulence: notably, some inferred coefficients become negative in certain regimes, indicating inverse transport, a phenomenon absent in conventional closure models. Moreover, the EHW-C model accurately reproduces the spectral and flux characteristics of high-resolution Direct Numerical Simulations (DNS), while requiring only one-eighth the spatial resolution per direction, yielding a tenfold speed-up. This work demonstrates how theory-guided machine learning can both enhance computational efficiency and uncover emergent transport mechanisms in strongly nonlinear plasma systems.

physics.plasm-ph↗

arXiv2025

A high-fidelity surrogate model for the ion temperature gradient (ITG) instability using a small expensive simulation dataset

One of the main challenges in building high-fidelity surrogate models of tokamak turbulence is the substantial demand for high-quality data. Typically, producing high-quality data involves simulating complex physical processes, which requires extensive computing resources. In this work, we propose a fine tuning-based approach to develop the surrogate model that reduces the amount of high-quality data required by 80\%. We demonstrate the effectiveness of this approach by constructing a proof-of-principle ITG surrogate model using datasets generated from two gyrokinetic codes, GKW and GX. GX needs in terms of computing resources are much lighter than GKW. Remarkably, the surrogate models' performance remain nearly the same whether trained on 798 GKW results alone or 159 GKW results plus an additional 11979 GX results. These encouraging outcomes indicate that fine tuning methods can significantly decrease the high-quality data needed to develop the simulation-driven surrogate model. Moreover, the approach presented here has the potential to facilitate surrogate model development for heavy codes and may ultimately pave the way for digital twin systems of tokamaks.

physics.plasm-ph↗

arXiv2024

Attention-aware convolutional neural networks for identification of magnetic islands in the tearing mode on EAST tokamak

The tearing mode, a large-scale MHD instability in tokamak, typically disrupts the equilibrium magnetic surfaces, leads to the formation of magnetic islands, and reduces core electron temperature and density, thus resulting in significant energy losses and may even cause discharge termination. This process is unacceptable for ITER. Therefore, the accurate identification of a magnetic island in real time is crucial for the effective control of the tearing mode in ITER in the future. In this study, based on the characteristics induced by tearing modes, an attention-aware convolutional neural network (AM-CNN) is proposed to identify the presence of magnetic islands in tearing mode discharge utilizing the data from ECE diagnostics in the EAST tokamak. A total of 11 ECE channels covering the range of core is used in the tearing mode dataset, which includes 2.5*10^9 data collected from 68 shots from 2016 to 2021 years. We split the dataset into training, validation, and test sets (66.5%, 5.7%, and 27.8%), respectively. An attention mechanism is designed to couple with the convolutional neural networks to improve the capability of feature extraction of signals. During the model training process, we utilized adaptive learning rate adjustment and early stopping mechanisms to optimize performance of AM-CNN. The model results show that a classification accuracy of 91.96% is achieved in tearing mode identification. Compared to CNN without AM, the attention-aware convolutional neural networks demonstrate great performance across accuracy, recall metrics, and F1 score. By leveraging the deep learning model, which incorporates a physical understanding of the tearing process to identify tearing mode behaviors, the combination of physical mechanisms and deep learning is emphasized, significantly laying an important foundation for the future intelligent control of tearing mode dynamics.

physics.plasm-ph↗

arXiv2024

Reconstruction of Poloidal Magnetic Fluxes on EAST based on Neural Networks with Measured Signals

The accurate construction of tokamak equilibria, which is critical for the effective control and optimization of plasma configurations, depends on the precise distribution of magnetic fields and magnetic fluxes. Equilibrium fitting codes, such as EFIT relying on traditional equilibrium algorithms, require solving the GS equation by iterations based on the least square method constrained with measured magnetic signals. The iterative methods face numerous challenges and complexities in the pursuit of equilibrium optimization. Furthermore, these methodologies heavily depend on the expertise and practical experience, demanding substantial resource allocation in personnel and time. This paper reconstructs magnetic equilibria for the EAST tokamak based on artificial neural networks through a supervised learning method. We use a fully connected neural network to replace the GS equation and reconstruct the poloidal magnetic flux distribution by training the model based on EAST datasets. The training set, validation set, and testing set are partitioned randomly from the dataset of poloidal magnetic flux distributions of the EAST experiments in 2016 and 2017 years. The feasibility of the neural network model is verified by comparing it to the offline EFIT results. It is found that the neural network algorithm based on the supervised machine learning method can accurately predict the location of different closed magnetic flux surfaces at a high efficiency. The similarities of the predicted X-point position and last closed magnetic surface are both 98%. The Pearson coherence of the predicted q profiles is 92%. Compared with the target value, the model results show the potential of the neural network model for practical use in plasma modeling and real-time control of tokamak operations.

physics.plasm-ph↗

arXiv2022

A data management system for machine learning research of tokamak

In recent years, machine learning (ML) research methods have received increasing attention in the tokamak community. The conventional database (i.e., MDSplus for tokamak) of experimental data has been designed for small group consumption and is mainly aimed at simultaneous visualization of a small amount of data. The ML data access patterns fundamentally differ from traditional data access patterns. The typical MDSplus database is increasingly showing its limitations. We developed a new data management system suitable for tokamak machine learning research based on Experimental Advanced Superconducting Tokamak (EAST) data. The data management system is based on MongoDB and Hierarchical Data Format version 5 (HDF5). Currently, the entire data management has more than 3000 channels of data. The system can provide highly reliable concurrent access. The system includes error correction, MDSplus original data conversion, and high-performance sequence data output. Further, some valuable functions are implemented to accelerate ML model training of fusion, such as bucketing generator, the concatenating buffer, and distributed sequence generation. This data management system is more suitable for fusion machine learning model R\&D than MDSplus, but it can not replace the MDSplus database. The MDSplus database is still the backend for EAST tokamak data acquisition and storage.

physics.plasm-ph↗

arXiv2022

EAST discharge prediction without integrating simulation results

In this work, a purely data-driven discharge prediction model was developed and tested without integrating any data or results from simulations. The model was developed based on the experimental data from the Experimental Advanced Superconducting Tokamak (EAST) campaign 2010-2020 discharges and can predict the actual plasma current $I_{p}$, normalized beta $β_{n}$, toroidal beta $β_{t}$, beta poloidal $β_{p}$, electron density $n_{e}$, store energy $W_{mhd}$, loop voltage $V_{loop}$, elongation at plasma boundary $κ$, internal inductance $l_{i}$, q at magnetic axis $q_{0}$, and q at 95% flux surface $q_{95}$. The average similarities of all the selected key diagnostic signals between prediction results and the experimental data are greater than 90%, except for the $V_{loop}$ and $q_{0}$. Before a tokamak experiment, the values of actuator signals are set in the discharge proposal stage, with the model allowing to check the consistency of expected diagnostic signals. The model can give the estimated values of the diagnostic signals to check the reasonableness of the tokamak experimental proposal.

physics.plasm-ph↗

arXiv2022

A machine-learning-based tool for last closed-flux surface reconstruction on tokamaks

Nuclear fusion represents one of the best alternatives for a sustainable source of clean energy. Tokamaks allow to confine fusion plasma with magnetic fields and one of the main challenges in the control of the magnetic configuration is the prediction/reconstruction of the Last Closed-Flux Surface (LCFS). The evolution in time of the LCFS is determined by the interaction of the actuator coils and the internal tokamak plasma. This task requires real-time capable tools able to deal with high-dimensional data as well as with high resolution in time, where the interaction between a wide range of input actuator coils with internal plasma state responses add additional layer of complexity. In this work, we present the application of a novel state of the art machine learning model to the LCFS reconstruction in the Experimental Advanced Superconducting Tokamak (EAST) that learns automatically from the experimental data of EAST. This architecture allows not only offline simulation and testing of a particular control strategy, but can also be embedded in the real-time control system for online magnetic equilibrium reconstruction and prediction. In the real-time modeling test, our approach achieves very high accuracies, with over 99% average similarity in LCFS reconstruction of the entire discharge process.

physics.plasm-ph↗

arXiv2022

High-Uniformity Calculation Method of Four-Coil Configuration in Large-Caliber Magnetic Field Immunity Testing System

Power electronic equipment regulated by the International Thermonuclear Experimental Reactor (ITER) organization must pass the relevant steady-state magnetic field immunity test. The main body of magnetic field immunity test is magnetic field generator coil. Through mathematical derivation in this paper, the magnetic field calculation formulas of four-coil configuration under ideal and actual models are obtained. The traditional method of magnetic field performance calculation is compared with the general formula method under the ideal model. A global parameter optimization method based on Lagrange Multiplier by KKT conditions is proposed to obtain the coil parameters of high-uniformity magnetic field. The magnetic field distribution in the uniform zone is revealed by the finite element method. The model analysis is proved to be correct and effective by experimental results. The research of this paper provides a practical scheme for the coil design with high magnetic field and high-quality uniformity.

physics.ins-det↗

arXiv2020

Experiment data-driven modeling of tokamak discharge in EAST

A model for tokamak discharge through deep learning has been done on a superconducting long-pulse tokamak (EAST). This model can use the control signals (i.e. Neutral Beam Injection (NBI), Ion Cyclotron Resonance Heating (ICRH), etc) to model normal discharge without the need for doing real experiments. By using the data-driven methodology, we exploit the temporal sequence of control signals for a large set of EAST discharges to develop a deep learning model for modeling discharge diagnostic signals, such as electron density $n_{e}$, store energy $W_{mhd}$ and loop voltage $V_{loop}$. Comparing the similar methodology, we use Machine Learning techniques to develop the data-driven model for discharge modeling rather than disruption prediction. Up to 95% similarity was achieved for $W_{mhd}$. The first try showed promising results for modeling of tokamak discharge by using the data-driven methodology. The data-driven methodology provides an alternative to physical-driven modeling for tokamak discharge modeling.

physics.plasm-ph↗