Deep Optimal Sensor Placement for Black Box Stochastic Simulations

• URL: http://arxiv.org/abs/2410.12036v1
• Date: Tue, 15 Oct 2024 20:10:25 GMT
• Title: Deep Optimal Sensor Placement for Black Box Stochastic Simulations
• Authors: Paula Cordero-Encinar, Tobias Schröder, Peter Yatsyshin, Andrew Duncan,
• Abstract summary: We propose a novel approach, the joint distribution over input parameters and solution with a joint energy-based model. We demonstrate the validity of our framework on a variety of problems, showing that our method provides highly informative sensor locations at a lower computational cost compared to conventional approaches.
• Score: 1.638332186726632
• License:
• Abstract: 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.

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