Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
- DOI
- 10.5194/gmd-15-3433-2022
- Published
- 2022-05-02
- Container
- Geoscientific Model Development
- Publisher
- Copernicus GmbH
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.5194/gmd-15-3433-2022,
title = {Efficient high-dimensional variational data assimilation with machine-learned reduced-order models},
author = {Romit Maulik and Vishwas Rao and Jiali Wang and Gianmarco Mengaldo and Emil Constantinescu and Bethany Lusch and Prasanna Balaprakash and Ian Foster and Rao Kotamarthi},
year = {2022},
journal = {Geoscientific Model Development},
doi = {10.5194/gmd-15-3433-2022},
url = {https://doi.org/10.5194/gmd-15-3433-2022}
}RIS
TY - JOUR TI - Efficient high-dimensional variational data assimilation with machine-learned reduced-order models AU - Romit Maulik AU - Vishwas Rao AU - Jiali Wang AU - Gianmarco Mengaldo AU - Emil Constantinescu AU - Bethany Lusch AU - Prasanna Balaprakash AU - Ian Foster AU - Rao Kotamarthi PY - 2022 JO - Geoscientific Model Development DO - 10.5194/gmd-15-3433-2022 UR - https://doi.org/10.5194/gmd-15-3433-2022 ER -
APA
Maulik, R., Rao, V., Wang, J., Mengaldo, G., Constantinescu, E., Lusch, B., Balaprakash, P., Foster, I., & Kotamarthi, R. (2022). Efficient high-dimensional variational data assimilation with machine-learned reduced-order models. Geoscientific Model Development. https://doi.org/10.5194/gmd-15-3433-2022
Source records
- crossref · retrieved 2026-09-27T14:16:38.544Z