mlsanityData to 14 Oct 2026

Deep Optimal Sensor Placement for Black Box Stochastic Simulations

arXiv 2410.12036 ↗First posted 15 Oct 2024stat.ML cs.LG stat.APcited by 0

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Abstract (arXiv)

Selecting cost-effective optimal sensor configurations for subsequent inference of parameters in black-box stochastic systems faces significant computational barriers. We propose a novel and robust approach, modelling the joint distribution over input parameters and solution with a joint energy-based model, trained on simulation data. Unlike existing simulation-based inference approaches, which must be tied to a specific set of point evaluations, we learn a functional representation of parameters and solution. This is used as a resolution-independent plug-and-play surrogate for the joint distribution, which can be conditioned over any set of points, permitting an efficient approach to sensor placement. We demonstrate the validity of our framework on a variety of stochastic problems, showing that our method provides highly informative sensor locations at a lower computational cost compared to conventional approaches.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 61 references that are in the map. First posted on arXiv 15 Oct 2024.

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31 of 61 references.

References · 31

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