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.
Code (1)
Tasks
Surface ReconstructionSimilar Papers 제목 키워드 기반
Machine Learning Emulation of Urban Land Surface Processes
Can we improve the modeling of urban land surface processes with machine learning (ML)? A prior comparison of urban land surface models (ULSMs) found that no single model is 'best' at predicting all common surface fluxes…
BIG-bench Machine LearningApplication of Machine Learning Methods in Inferring Surface Water Groundwater Exchanges using High Temporal Resolution Temperature Measurements
We examine the ability of machine learning (ML) and deep learning (DL) algorithms to infer surface/ground exchange flux based on subsurface temperature observations. The observations and fluxes are produced from a high-r…
Surface Flux Transport Modeling using Physics Informed Neural Networks
Studying the magnetic field properties on the solar surface is crucial for understanding the solar and heliospheric activities, which in turn shape space weather in the solar system. Surface Flux Transport (SFT) modeling…
EFLUX: Elastic Multi-Robot Formation Navigation and Adaptation with Agentic LLMs
Multi-robot teams operating in confined or cluttered environments must adapt both their formation geometry and group topology to navigate through complex obstacles. This adaptation requires two complementary behaviors: d…
Searching the Landscape of Flux Vacua with Genetic Algorithms
In this paper, we employ genetic algorithms to explore the landscape of type IIB flux vacua. We show that genetic algorithms can efficiently scan the landscape for viable solutions satisfying various criteria. More speci…