A Bayesian latent GP calibration framework for aerodynamic surrogates marginalizes input uncertainty and matches output uncertainty statistics, achieving 94.2-95.8% coverage of true 95% intervals.
Computer model calibration using high-dimensional output.Journal of the American Statistical Asso- ciation, 103(482):570–583, 2008
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
GPLFR is a Gaussian process model that analytically marginalizes decoder weights to couple latent factor compression with prediction for high-dimensional low-data regression, demonstrated via the first spatially resolved emulator of rocky exoplanet global climate models.
citing papers explorer
-
A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification
A Bayesian latent GP calibration framework for aerodynamic surrogates marginalizes input uncertainty and matches output uncertainty statistics, achieving 94.2-95.8% coverage of true 95% intervals.
-
Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
GPLFR is a Gaussian process model that analytically marginalizes decoder weights to couple latent factor compression with prediction for high-dimensional low-data regression, demonstrated via the first spatially resolved emulator of rocky exoplanet global climate models.