Modelling spatiotemporal dynamics from Earth observation data with neural differential equations
Author:
Funder
ANR Project LOCUST
CLEAR joint Lab. Thales - Sorbonne Universite
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s10994-022-06139-2.pdf
Reference50 articles.
1. Alvarez, M. A., Luengo, D., & Lawrence, N. D. (2013). Linear latent force models using Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(11), 2693–2705.
2. Béréziat, D., & Herlin, I. (2015). Coupling dynamic equations and satellite images for modelling ocean surface circulation (pp. 191–205). Springer.
3. Bergen, K. J., Johnson, P. A., De Hoop, M. V., & Beroza, G. C. (2019). Machine learning for data-driven discovery in solid Earth geoscience. Science, 363, 6433.
4. Bocquet, M. (2012). Parameter-field estimation for atmospheric dispersion: Application to the Chernobyl accident using 4D-Var. Quarterly Journal of the Royal Meteorological Society, 138(664), 664–681. https://doi.org/10.1002/qj.961
5. Carrassi, A., Bocquet, M., Bertino, L., & Evensen, G. (2018a). Data assimilation in the geosciences: An overview of methods, issues, and perspectives. Wiley Interdisciplinary Reviews: Climate Change, 9(5). https://doi.org/10.1002/wcc.535
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