Adaptive Gaussian Process-Based strategies for Solving the NASA-DNV UQ Challenge 2025
This paper describes a dedicated approach to solve the 2025 NASA-DNV UQ challenge problem using adaptive Gaussian process strategies.The uncertainty model is determined through a calibration problem using an optimization approach to identify the aleatory variable joint distribution and the epistemic variable uncertainties.The estimation of the prediction interval for the model output components consists of a quantile estimation problem based on an adaptive Gaussian process strategy.Eventually, the design optimization problems are solved using Bayesian optimization controlling the noise level involved in the estimation of the objective and constraint functions.
Authors
- Julien Demange-Chryst
- Mathieu Balesdent (ORCID: https://orcid.org/0000-0003-4064-3361)
- Jérôme Morio (ORCID: https://orcid.org/0000-0002-8811-8956)
- Nathalie Bartoli (ORCID: https://orcid.org/0000-0002-6451-2203)
- Loïc Brevault (ORCID: https://orcid.org/0000-0001-8081-6082)
- Sylvain Dubreuil (ORCID: https://orcid.org/0000-0002-3013-0741)
Institutions
- École Nationale de l’Aviation Civile (FR)
- Université Fédérale de Toulouse Midi-Pyrénées (FR)
- Université Paris-Saclay (FR)
- Centre Inria de Saclay (FR)
- National Higher French Institute of Aeronautics and Space (FR)
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.3850/978-981-94-3281-3_esrel-sra-e2025-p7226-cd
- Primary Topic
- Inertial Sensor and Navigation
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Office National d'études et de Recherches Aérospatiales