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. This is joint work with Tobias Schröder, Peter Yatsyshin and Andrew Duncan

Bio: Paula is a second-year PhD student at Imperial College London, affiliated with the Centre for Doctoral Training in Statistics and Machine Learning (StatML). Her research focuses on generative models, their connection to sampling methods, and applications in AI for scientific discovery (AI4Science).

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