- Series
- Applied and Computational Mathematics Seminar
- Time
- Monday, February 20, 2023 - 2:00pm for 1 hour (actually 50 minutes)
- Location
- Skiles 005 and https://gatech.zoom.us/j/98355006347
- Speaker
- Peng Chen – Georgia Tech CSE – pchen402@gatech.edu – https://faculty.cc.gatech.edu/~pchen402/
- Organizer
- Wenjing Liao
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.