Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models
- DOI
- 10.1051/meca/2022001
- Published
- 2022
- Container
- Mechanics & Industry
- Publisher
- EDP Sciences
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1051/meca/2022001,
title = {Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models},
author = {Thomas Daniel and Fabien Casenave and Nissrine Akkari and David Ryckelynck and Christian Rey},
year = {2022},
journal = {Mechanics \& Industry},
doi = {10.1051/meca/2022001},
url = {https://doi.org/10.1051/meca/2022001}
}RIS
TY - JOUR TI - Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models AU - Thomas Daniel AU - Fabien Casenave AU - Nissrine Akkari AU - David Ryckelynck AU - Christian Rey PY - 2022 JO - Mechanics & Industry DO - 10.1051/meca/2022001 UR - https://doi.org/10.1051/meca/2022001 ER -
APA
Daniel, T., Casenave, F., Akkari, N., Ryckelynck, D., & Rey, C. (2022). Uncertainty quantification for industrial numerical simulation using dictionaries of reduced order models. Mechanics & Industry. https://doi.org/10.1051/meca/2022001
Source records
- crossref · retrieved 2026-09-25T05:27:04.142Z