Diffusion-Inversion-Net (DIN): An End-to-End Direct Probabilistic Framework for Characterizing Hydraulic Conductivities and Quantifying Uncertainty

Diffusion-Inversion-Net (DIN) is a deep learning framework for probabilistic inversion of groundwater flow and solute transport processes. It utilizes diffusion models as geological priors and directly generates posterior parameter ensembles without iterative forward simulations.扩散反演网络（Diffusion-Inversion-Net，DIN）是一款用于地下水流动与溶质运移过程概率反演的深度学习框架。该框架以扩散模型作为地质先验，无需进行迭代正向模拟即可直接生成后验参数集合。