Non‐uniform active learning for Gaussian process models with applications to trajectory informed aerodynamic databases

The ability to non‐uniformly weight the input space is desirable for many applications, and has been explored for space‐filling approaches. Increased interests in linking models, such as in a digital twinning framework, increases the need for sampling emulators where they are most likely to be evalu...

Full description

Saved in:
Bibliographic Details
Published in:Statistical analysis and data mining 2024-04, Vol.17 (2), p.n/a
Main Authors: Quinlan, Kevin R., Movva, Jagadeesh, Perfect, Brad
Format: Article
Language:English
Subjects:
active learning
aerodynamics
Atmospheric entry
Atmospheric models
ENGINEERING
Gaussian process
Learning
MATHEMATICS AND COMPUTING
non‐uniform sampling
Sampling methods
Weighting functions
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The ability to non‐uniformly weight the input space is desirable for many applications, and has been explored for space‐filling approaches. Increased interests in linking models, such as in a digital twinning framework, increases the need for sampling emulators where they are most likely to be evaluated. In particular, we apply non‐uniform sampling methods for the construction of aerodynamic databases. This paper combines non‐uniform weighting with active learning for Gaussian Processes (GPs) to develop a closed‐form solution to a non‐uniform active learning criterion. We accomplish this by utilizing a kernel density estimator as the weight function. We demonstrate the need and efficacy of this approach with an atmospheric entry example that accounts for both model uncertainty as well as the practical state space of the vehicle, as determined by forward modeling within the active learning loop.
ISSN:1932-1864
1932-1872
DOI:10.1002/sam.11675