CARONTE: a Physics-Informed Extreme Learning Machine-Based Algorithm for Plasma Boundary Reconstruction in Magnetically Confined Fusion Devices

TL;DR

This paper introduces a physics-informed neural network algorithm using Extreme Learning Machines for real-time plasma boundary reconstruction in tokamaks, outperforming traditional methods in accuracy, robustness, and adaptability.

Contribution

It presents a novel, real-time, physics-informed neural network approach for plasma boundary reconstruction that generalizes well across different equilibria without extensive training.

Findings

Accurately reconstructs plasma boundaries in complex configurations.

Outperforms established algorithms like those used at JET.

Demonstrates robustness to measurement noise.

Abstract

In this work, we propose a novel physics informed neural network based algorithm for real time plasma boundary reconstruction in tokamak devices. The approach is based on a single Extreme Learning Machine network used to solve the homogeneous Grad Shafranov equation, which is required to identify the plasma boundary. This architecture enables the real time training of the network parameters using the available magnetic sensor data and, consequently, dynamically adapting the network output to the evolving plasma equilibrium. We demonstrate that, the network performs accurate plasma boundary reconstruction for complex configurations, outperforming well established methods, such as the algorithm used for decades at the Joint European Torus, the world's largest tokamak, until it ceased operation in 2023. Indeed, compared to the latter, the proposed solution better generalizes the poloidal flux function, without requiring algorithm retuning across different plasma equilibria. The proposed neural network reconstructor demonstrates also greater robustness with respect to noise on the magnetic measurements. Moreover, this method takes advantage of the generalization power of neural networks but without the need for extensive, time consuming training based on a huge amount of experimental data, making its implementation on existing devices straightforward.