An active learning framework for reduced-order models, BayPOD-AL, shows that an error-bounded acquisition function outperforms uncertainty sampling and random sampling on a 1D heat equation surrogate task.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Cost-effective Reduced-Order Modeling via Bayesian Active Learning
An active learning framework for reduced-order models, BayPOD-AL, shows that an error-bounded acquisition function outperforms uncertainty sampling and random sampling on a 1D heat equation surrogate task.