用物理方程约束扩散模型，提升气温高分辨率重建的合理性
A PDE-Informed Latent Diffusion Model for 2-m Temperature Downscaling
- 在扩散模型中加入偏微分方程损失，强制生成结果符合大气热平流-扩散平衡
- 在真实气象数据上验证，模型生成的温度场物理残差显著降低
- 适合气候模拟、气象预报等需要物理一致性的高分辨率建模场景
本文提出一种基于物理约束的潜空间扩散模型，用于大气数据的动力降尺度，重点是重建高分辨率2米气温场。在已有扩散架构基础上，采用相对于参考UNet的残差形式，并将偏微分方程（PDE）损失项引入训练目标。该损失在全分辨率（像素）空间计算，通过有限差分近似实现有效平流-扩散平衡的物理一致性约束。实验表明，传统扩散训练已产生较低的PDE残差，进一步微调该损失可进一步正则化模型，提升生成场的物理合理性。全部代码已开源，便于后续研究与开发。
原文摘要 · Abstract (English)
This work presents a physics-conditioned latent diffusion model tailored for dynamical downscaling of atmospheric data, with a focus on reconstructing high-resolution 2-m temperature fields. Building upon a pre-existing diffusion architecture and employing a residual formulation against a reference UNet, we integrate a partial differential equation (PDE) loss term into the model's training objective. The PDE loss is computed in the full resolution (pixel) space by decoding the latent representation and is designed to enforce physical consistency through a finite-difference approximation of an effective advection-diffusion balance. Empirical observations indicate that conventional diffusion training already yields low PDE residuals, and we investigate how fine-tuning with this additional loss further regularizes the model and enhances the physical plausibility of the generated fields. The entirety of our codebase is available on Github, for future reference and development.
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。