Joint Optimization of Sensor Hardware and Adaptive Measurement Strategies Using Dynamic Programming
A new preprint on arXiv introduces 'joint dynamic programming' (joint-DP), a framework that simultaneously optimizes physical sensor geometry and adaptive measurement strategies to maximize information capture. The work addresses a fundamental bottleneck at the analog-to-digital interface, where information lost by hardware cannot be recovered by downstream algorithms. The approach could enable more intelligent physical sensing systems, potentially scaling to complex photonic designs with over 100,000 design pixels.
Researchers have posted a preprint to arXiv proposing a unified optimization framework called joint dynamic programming (joint-DP), which co-designs sensor hardware geometry alongside a Bellman-optimal adaptive measurement policy. The motivation is a recognized bottleneck in sensing systems: information not captured at the hardware level is permanently lost and cannot be recovered by any subsequent digital processing. Prior work has typically addressed this either by optimizing sensor hardware or by developing adaptive measurement strategies on fixed hardware, but rarely both simultaneously. The framework derives hardware gradients through differentiable dynamic programming using a sharp Bellman maximum, and introduces a hierarchy of relaxations to extend applicability from small discrete partially observable Markov decision processes (POMDPs) to freeform photonic topologies with more than 100,000 design pixels. The work sits at the intersection of optics, optimization and control, computational physics, and quantum physics. As a preprint, the results have not yet undergone formal peer review.
What's missing
The paper has not yet undergone peer review, so independent verification of the claimed performance advantages over non-adaptive baselines is pending.
What different sources said
- arXiv physicsCenter
Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference
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