Deep Optimal Sensor Placement for Black Box Stochastic Simulations

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.

@article{cordero-encinar2025_2410.12036,
  title={ Deep Optimal Sensor Placement for Black Box Stochastic Simulations },
  author={ Paula Cordero-Encinar and Tobias Schröder and Peter Yatsyshin and Andrew Duncan },
  journal={arXiv preprint arXiv:2410.12036},
  year={ 2025 }
}