Taking on the NASA and DNV Challenge 2025: Bayesian Calibration and Optimization Under Hybrid Uncertainty
This paper addresses the NASA and DNV challenge on optimization under uncertainty, where participants were tasked with calibrating the uncertainty models of aleatory and epistemic parameters of an unknown system using a computational model and synthetic data, and identifying control parameters for different objectives.We present two approaches for model calibration, namely Bayesian optimization and sequential Bayesian updating.Additionally, a reliability-based optimization scheme based on a Bayesian approach and subset simulation is used to tackle a design optimization problem.
Authors
- Marcos A. Valdebenito (ORCID: https://orcid.org/0000-0002-5083-0454)
- Chao Dang (ORCID: https://orcid.org/0000-0001-7412-6309)
- Zhouzhou Song (ORCID: https://orcid.org/0000-0002-5899-6205)
- Marius Bittner (ORCID: https://orcid.org/0000-0001-5156-3821)
- Matthias G.R. Faes (ORCID: https://orcid.org/0000-0003-3341-3410)
- Lukas Fritsch
- Matteo Broggi (ORCID: https://orcid.org/0000-0002-3683-3907)
- Pei-Pei Li (ORCID: https://orcid.org/0000-0002-9766-297X)
- Jan Grashorn (ORCID: https://orcid.org/0000-0003-1820-1372)
- Nataly A. Manque (ORCID: https://orcid.org/0009-0004-5041-1833)
- Thomas Potthast
Publication Details
- Published
- 2025-01-01
- DOI
- https://doi.org/10.3850/978-981-94-3281-3_esrel-sra-e2025-p9016-cd
- Primary Topic
- Reservoir Engineering and Simulation Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Alexander von Humboldt-Stiftung
- European Commission
- Deutsche Forschungsgemeinschaft