AI RESEARCH
Christoffel-DPS: Optimal sensor placement in diffusion posterior sampling for arbitrary distributions
arXiv CS.LG
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ArXi:2605.06861v1 Announce Type: new State estimation is a critical task in scientific, engineering and control applications. Since the reliability of reconstructions depends on the number and position of sensors, optimal sensor placement (OSP) is essential in scenarios where measurements are sparse and expensive. Classical OSP approaches rely on Gaussian assumptions and are consequently unable to account for the complex distributions encountered in many real-world systems.