FreqNO-DPS corrects neural operator spectral bias in 3D elastic wavefield prediction by frequency-dependent guidance in diffusion posterior sampling conditioned on sparse observations, achieving near-zero bias at 2-5% sensor coverage.
Decoupled diffusion sampling for inverse problems on function spaces.arXiv preprint arXiv:2601.23280,
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Christoffel-DPS is a distribution-free optimal sensor placement framework for diffusion posterior sampling that provides non-asymptotic recovery bounds and outperforms Gaussian baselines on non-Gaussian benchmarks.
GRIFDIR proposes graph resolution-invariant FEM diffusion models that maintain resolution invariance and high fidelity on complex irregular domains.
DiLO turns diffusion sampling into deterministic latent optimization to satisfy the manifold consistency requirement for neural operators in inverse problem solving.
Distilled one-step consistency model from optimal-transport flow-matching teacher reconstructs high-fidelity dynamical system flows from low-fidelity data with 12x speedup, half the parameters, and 23.1% better SSIM than scratch-trained baselines.
citing papers explorer
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Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations
FreqNO-DPS corrects neural operator spectral bias in 3D elastic wavefield prediction by frequency-dependent guidance in diffusion posterior sampling conditioned on sparse observations, achieving near-zero bias at 2-5% sensor coverage.
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Christoffel-DPS: Optimal sensor placement in diffusion posterior sampling for arbitrary distributions
Christoffel-DPS is a distribution-free optimal sensor placement framework for diffusion posterior sampling that provides non-asymptotic recovery bounds and outperforms Gaussian baselines on non-Gaussian benchmarks.
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GRIFDIR: Graph Resolution-Invariant FEM Diffusion Models in Function Spaces over Irregular Domains
GRIFDIR proposes graph resolution-invariant FEM diffusion models that maintain resolution invariance and high fidelity on complex irregular domains.
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DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems
DiLO turns diffusion sampling into deterministic latent optimization to satisfy the manifold consistency requirement for neural operators in inverse problem solving.
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Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems
Distilled one-step consistency model from optimal-transport flow-matching teacher reconstructs high-fidelity dynamical system flows from low-fidelity data with 12x speedup, half the parameters, and 23.1% better SSIM than scratch-trained baselines.