Deep Optimal Sensor Placement for Black Box Stochastic Simulations

Paula Cordero Encinar, Tobias Schröder, Peter Yatsyshin, Andrew B. Duncan

TL;DR: An efficient Bayesian approach to sensor placement for black-box stochastic simulations using a resolution-invariant joint energy-based model.

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.

Submission Number: 834