用对数高斯过程提升等离子体诊断中非负物理量的重建精度
Nonlinear Gaussian process tomography with imposed non-negativity constraints on physical quantities for plasma diagnostics
- 引入对数高斯过程，通过解析方法强制非负约束
- 在RT-1装置上重建误差低于标准GPT和MFI方法
- 适合需要物理一致性约束的等离子体逆问题研究
我们提出一种新型断层成像方法——非线性高斯过程断层成像（nonlinear GPT），利用拉普拉斯近似对等离子体光学诊断中的发射率等非负物理量施加约束。此前原始GPT方法通过采样实现正后验分布，而本方法采用对数高斯过程（log-GP）实现更快计算与更自然的非负性强制。在环形阱装置RT-1的案例研究中，log-GPT在重建精度上优于标准GPT和最小费舍尔信息（MFI）方法，验证了非线性GPT在施加物理约束方面的有效性。
原文摘要 · Abstract (English)
We propose a novel tomographic method, nonlinear Gaussian process tomography (nonlinear GPT), that uses the Laplace approximation to impose constraints on non-negative physical quantities, such as the emissivity in plasma optical diagnostics. While positive-valued posteriors have previously been introduced through sampling-based approaches in the original GPT method, our alternative approach implements a logarithmic Gaussian process (log-GP) for faster computation and more natural enforcement of non-negativity. The effectiveness of the proposed log-GP tomography is demonstrated through a case study using the Ring Trap 1 (RT-1) device, where log-GPT outperforms existing methods, standard GPT, and the Minimum Fisher Information (MFI) methods in terms of reconstruction accuracy. The results highlight the effectiveness of nonlinear GPT for imposing physical constraints in applications to an inverse problem.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。