|Title: Scalable Bayesian optimal experimental design for efficient data acquisition|
|Speaker: Peng Chen of Georgia Tech|
|Contact: Matthias Chung, email@example.com|
|Date: 2022-11-10 at 10:00AM|
|Venue: MSC W301|
Bayesian optimal experimental design (OED) is a principled framework for maximizing information gained from limited data in Bayesian inverse problems. Unfortunately, conventional methods for OED are prohibitive when applied to expensive models with high-dimensional parameters. In this talk, I will present fast and scalable computational methods for large-scale Bayesian OED with infinite-dimensional parameters, including data-informed low-rank approximation, efficient offline-online decomposition, projected neural network approximation, and a new swapping greedy algorithm for combinatorial optimization.
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