arXiv subjects

Jiangang Li

Publications and source records attributed to Jiangang Li.

8 recordsLinked to original sources

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

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↗

arXiv2023

Real-Time Progressive Learning: Accumulate Knowledge from Control with Neural-Network-Based Selective Memory

Memory, as the basis of learning, determines the storage, update and forgetting of knowledge and further determines the efficiency of learning. Featured with the mechanism of memory, a radial basis function neural network based learning control scheme named real-time progressive learning (RTPL) is proposed to learn the unknown dynamics of the system with guaranteed stability and closed-loop performance. Instead of the Lyapunov-based weight update law of conventional neural network learning control (NNLC), which mainly concentrates on stability and control performance, RTPL employs the selective memory recursive least squares (SMRLS) algorithm to update the weights of the neural network and achieves the following merits: 1) improved learning speed without filtering, 2) robustness to hyperparameter setting of neural networks, 3) good generalization ability, i.e., reuse of learned knowledge in different tasks, and 4) guaranteed learning performance under parameter perturbation. Moreover, RTPL realizes continuous accumulation of knowledge as a result of its reasonably allocated memory while NNLC may gradually forget knowledge that it has learned. Corresponding theoretical analysis and simulation studies demonstrate the effectiveness of RTPL.

eess.SY↗

arXiv2023

Selective Memory Recursive Least Squares: Recast Forgetting into Memory in RBF Neural Network Based Real-Time Learning

In radial basis function neural network (RBFNN) based real-time learning tasks, forgetting mechanisms are widely used such that the neural network can keep its sensitivity to new data. However, with forgetting mechanisms, some useful knowledge will get lost simply because they are learned a long time ago, which we refer to as the passive knowledge forgetting phenomenon. To address this problem, this paper proposes a real-time training method named selective memory recursive least squares (SMRLS) in which the classical forgetting mechanisms are recast into a memory mechanism. Different from the forgetting mechanism, which mainly evaluates the importance of samples according to the time when samples are collected, the memory mechanism evaluates the importance of samples through both temporal and spatial distribution of samples. With SMRLS, the input space of the RBFNN is evenly divided into a finite number of partitions and a synthesized objective function is developed using synthesized samples from each partition. In addition to the current approximation error, the neural network also updates its weights according to the recorded data from the partition being visited. Compared with classical training methods including the forgetting factor recursive least squares (FFRLS) and stochastic gradient descent (SGD) methods, SMRLS achieves improved learning speed and generalization capability, which are demonstrated by corresponding simulation results.

eess.SY↗

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↗

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↗