Combining data assimilation and machine learning to build data-driven models for unknown long time dynamics-Applications in cardiovascular modeling.
- DOI
- 10.1002/cnm.3471
- Published
- 2021 Jul
- Container
- International journal for numerical methods in biomedical engineering
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1002/cnm.3471,
title = {Combining data assimilation and machine learning to build data-driven models for unknown long time dynamics-Applications in cardiovascular modeling.},
author = {Regazzoni F and Chapelle D and Moireau P},
year = {2021},
journal = {International journal for numerical methods in biomedical engineering},
doi = {10.1002/cnm.3471},
url = {https://doi.org/10.1002/cnm.3471}
}RIS
TY - JOUR TI - Combining data assimilation and machine learning to build data-driven models for unknown long time dynamics-Applications in cardiovascular modeling. AU - Regazzoni F AU - Chapelle D AU - Moireau P PY - 2021 JO - International journal for numerical methods in biomedical engineering DO - 10.1002/cnm.3471 UR - https://doi.org/10.1002/cnm.3471 ER -
APA
F, R., D, C., & P, M. (2021). Combining data assimilation and machine learning to build data-driven models for unknown long time dynamics-Applications in cardiovascular modeling.. International journal for numerical methods in biomedical engineering. https://doi.org/10.1002/cnm.3471
Source records
- pubmed · retrieved 2026-09-25T00:26:50.186Z