Comparing machine learning and inverse modeling approaches for the source term estimation
Author:
Funder
National Science Foundation
Defense Threat Reduction Agency
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11869-024-01570-x.pdf
Reference21 articles.
1. Alessandrini S, Ferrero E, Pertot C, Orlandi E (2005) Comparison of different dispersion models with tracer experiment. Nuovo Cimento della Societa Italiana di Fisica C 28:141. https://doi.org/10.1393/ncc/i2005-10187-0
2. Bieringer PE, Rodriguez LM, Vandenberghe F et al (2015) Automated source term and wind parameter estimation for atmospheric transport and dispersion applications. Atmos Environ 122:206. https://doi.org/10.1016/j.atmosenv.2015.09.016
3. Enting IG (2002) Inverse Problems in Atmospheric Constituent Transport. Cambridge University Press
4. Fanfarillo A (2019) Quantifying uncertainty in source term estimation with tensorflow probability. In: Proceedings of UrgentHPC 2019: 1st International Workshop on HPC for Urgent Decision Making - Held in conjunction with SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis
5. Jaffe D, Anderson T, Covert D et al (1999) Transport of Asian air pollution to North America. Geophys Res Lett 26:711. https://doi.org/10.1029/1999GL900100
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