Paper ID: 2412.00087

ONION: Physics-Informed Deep Learning Model for Line Integral Diagnostics Across Fusion Devices

Cong Wang, Weizhe Yang, Haiping Wang, Renjie Yang, Jing Li, Zhijun Wang, Xinyao Yu, Yixiong Wei, Xianli Huang, Zhaoyang Liu, Changqing Zou, Zhifeng Zhao

This paper introduces a Physics-Informed model architecture that can be adapted to various backbone networks. The model incorporates physical information as additional input and is constrained by a Physics-Informed loss function. Experimental results demonstrate that the additional input of physical information substantially improve the model's ability with a increase in performance observed. Besides, the adoption of the Softplus activation function in the final two fully connected layers significantly enhances model performance. The incorporation of a Physics-Informed loss function has been shown to correct the model's predictions, bringing the back-projections closer to the actual inputs and reducing the errors associated with inversion algorithms. In this work, we have developed a Phantom Data Model to generate customized line integral diagnostic datasets and have also collected SXR diagnostic datasets from EAST and HL-2A. The code, models, and some datasets are publicly available at this https URL

Submitted: Nov 27, 2024