5 citations · 5 across the 2 of their papers we have counts for
3 papers
cs.CE2024
Sequential infinite-dimensional Bayesian optimal experimental design with derivative-informed latent attention neural operator
Jinwoo Go, Peng Chen
We develop a new computational framework to solve sequential Bayesian optimal experimental design (SBOED) problems constrained by large-scale partial differential equations with in…
cs.LG2024★ 5 cited
Probabilistic Bayesian optimal experimental design using conditional normalizing flows
Rafael Orozco, Felix J. Herrmann, Peng Chen
Bayesian optimal experimental design (OED) seeks to conduct the most informative experiment under budget constraints to update the prior knowledge of a system to its posterior from…
cs.CE2023
Accurate, scalable, and efficient Bayesian optimal experimental design with derivative-informed neural operators
Jinwoo Go, Peng Chen
We consider optimal experimental design (OED) problems in selecting the most informative observation sensors to estimate model parameters in a Bayesian framework. Such problems are…