DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems

Diffusion models have emerged as powerful generative priors for solving PDE-constrained inverse problems. Compared with end-to-end approaches that learn inverse mappings from large paired observation--parameter datasets, decoupling the parameter prior from the forward physical model reduces reliance