This paper presents EFIT-mini, a novel equilibrium reconstruction algorithm which integrates neural networks with physical simulation, enabling real-time plasmas control in the EXL-50U tokamak. By synergizing the high accuracy and physical principles of traditional Grad–Shafranov equation solvers with the superior numerical stability of pure data-driven machine learning approaches, EFIT-mini fundamentally resolves their respective limitations while preserving real-time performance, achieving enhanced inversion accuracy, speed, stability, and development efficiency. Validated on EXL-50U experimental data, EFIT-mini performs over 98% overlap ratio in last closed flux surface reconstruction accuracy compared to offline-EFIT. Besides, EFIT-mini takes only 0.36 ms per time slice for the 129×129 resolution inversion. Real-time implementation on the EXL-50U tokamak confirms robust generalization capabilities of EFIT-mini, showing consistent accuracy even for discharge scenarios significantly deviating from the training dataset. Furthermore, the algorithm successfully drives proportional-integral-derivative feedback control of plasmas horizontal positioning based on its real-time reconstructions. By harmonizing machine learning’s computational stability with physics-based interpretability, this hybrid approach establishes a reliable framework for real-time equilibrium reconstruction.
This paper presents a new algorithm called EFIT-mini that combines neural networks and physical simulations to quickly and accurately reconstruct the shape of plasma in a tokamak fusion reactor. It achieves high accuracy, real-time performance, and stability, overcoming the limitations of traditional methods.