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Scalable sequential design for Bayesian inverse problems via conditional transport

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RCLW04 - Early Career Pioneers in Uncertainty Quantification and AI for Science

We present a scalable approach to sequential optimal experimental design for Bayesian inverse problems with expensive forward models and high-dimensional parameters. By combining transport maps, a derivative-based upper bound on expected information gain, and dimension reduction via likelihood-informed subspaces, our method enables tractable experimental design in a sequential setting. We demonstrate the effectiveness of the approach with examples from groundwater flow and photoacoustic imaging.This talk is based on joint work with Tiangang Cui, Roland Herzog, and Robert Scheichl. 

This talk is part of the Isaac Newton Institute Seminar Series series.

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