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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DSMC-based neighborhood analysis of rarefied hypersonic flow over triangular protrusions shows quantity-dependent bulk-to-wall information requirements, with no universal length scale for surface loading.
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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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Quantity-Dependent Bulk-to-Wall Observability of Surface Loading in Rarefied Hypersonic Flow over Triangular Protrusions
DSMC-based neighborhood analysis of rarefied hypersonic flow over triangular protrusions shows quantity-dependent bulk-to-wall information requirements, with no universal length scale for surface loading.