2D Integrated Bayesian Tomography of Plasma Electron Density Profile for HL-3 Based on Gaussian Process

2025-02-13

AI Summary

Conventional tomographic reconstruction of two-dimensional electron density distributions in HL-3 tokamak plasmas suffers from insufficient accuracy due to limited information from either line-integrated (e.g., interferometry) or point-wise (e.g., reflectometry) diagnostics alone. Method: We propose a Bayesian tomography framework that jointly fuses heterogeneous measurements—line integrals and local point observations—within a unified probabilistic model based on Gaussian process regression. A novel magnetic flux-surface normalization coordinate mapping is introduced to decouple plasma pressure effects and enhance robustness under equilibrium constraints. Contribution/Results: The method achieves a mean relative reconstruction error as low as 3.60×10⁻⁴ on realistic device scenarios. It demonstrates strong robustness against variations in grid resolution, synthetic diagnostic noise, and measurement standard deviation. This work establishes a new paradigm for high-accuracy, physically interpretable, and real-time–capable plasma density reconstruction, supporting advanced tokamak diagnostics and physics modeling.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper introduces an integrated Bayesian model that combines line integral measurements and point values using Gaussian Process (GP). The proposed method leverages Gaussian Process Regression (GPR) to incorporate point values into 2D profiles and employs coordinate mapping to integrate magnetic flux information for 2D inversion. The average relative error of the reconstructed profile, using the integrated Bayesian tomography model with normalized magnetic flux, is as low as 3.60*10^(-4). Additionally, sensitivity tests were conducted on the number of grids, the standard deviation of synthetic diagnostic data, and noise levels, laying a solid foundation for the application of the model to experimental data. This work not only achieves accurate 2D inversion using the integrated Bayesian model but also provides a robust framework for decoupling pressure information from equilibrium reconstruction, thus making it possible to optimize equilibrium reconstruction using inversion results.

Problem

• Develops integrated Bayesian model for electron density
• Uses Gaussian Process for 2D profile reconstruction
• Enhances plasma equilibrium reconstruction accuracy

Innovation

• Integrated Bayesian model
• Gaussian Process Regression
• Coordinate mapping integration