用量子桥模型实现城市气温超分辨率，速度更快、不确定性更准。
Probabilistic Super-Resolution for Urban Micrometeorology via a Schrödinger Bridge
- 用薛定谔桥神经网络直接从低分辨率数据生成高分辨率气温图。
- 相比扩散模型，计算量降为1/5，精度相当，且生成结果方差更大。
- 适合需要快速、可靠气温预测的智慧城市建设与气象应用。
本研究采用神经网络求解薛定谔桥问题，实现城市区域2米气温的超分辨率重建。薛定谔桥基于扩散过程描述两组数据分布间的变换。所提出的薛定谔桥模型（SM）直接将低分辨率数据转换为高分辨率数据，不同于需从高斯噪声生成的扩散模型（DMs）。低分辨率与高分辨率数据均来自同一物理模型在相同初边值条件下的独立数值模拟。相较于扩散模型，该方法在保持相近精度的同时，计算成本仅为1/5：每样本仅需10次神经网络评估，而扩散模型需50次。此外，SM生成的高分辨率样本具有更大方差，表明其在不确定性量化方面表现更优。由于计算效率显著提升，该方法为基于超分辨率的实时城市微气象集合预测提供了可行性。
原文摘要 · Abstract (English)
This study employs a neural network that represents the solution to a Schrödinger bridge problem to perform super-resolution of 2-m temperature in an urban area. Schrödinger bridges generally describe transformations between two data distributions based on diffusion processes. We use a specific Schrödinger-bridge model (SM) that directly transforms low-resolution data into high-resolution data, unlike denoising diffusion probabilistic models (simply, diffusion models; DMs) that generate high-resolution data from Gaussian noise. Low-resolution and high-resolution data were obtained from separate numerical simulations with a physics-based model under common initial and boundary conditions. Compared with a DM, the SM attains comparable accuracy at one-fifth the computational cost, requiring 50 neural-network evaluations per datum for the DM and only 10 for the SM. Furthermore, high-resolution samples generated by the SM exhibit larger variance, implying superior uncertainty quantification relative to the DM. Owing to the reduced computational cost of the SM, our results suggest the feasibility of real-time ensemble micrometeorological prediction using SM-based super-resolution.
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