Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems

We propose latent-space diffusion posterior sampling (L-DPS), an approximate Bayesian framework for high-dimensional inverse problems governed by partial differential equations (PDEs). The method addresses three challenges in PDE-constrained inversion: implicit sample-based priors without tractable …

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