Assimilation of statistical data into turbulent flows using physics-informed neural networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Surfaces and Interfaces,General Materials Science,General Chemistry,Biophysics,Biotechnology
Link
https://link.springer.com/content/pdf/10.1140/epje/s10189-023-00268-9.pdf
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3. G. Evensen, Data Assimilation: The Ensemble Kalman Filter (Springer, Berlin, 2006). (Google-Books-ID: VJ2oOecHhOYC)
4. C. Cotter, D. Crisan, D. Holm, W. Pan, I. Shevchenko, Data assimilation for a quasi-geostrophic model with circulation-preserving stochastic transport noise. J. Stat. Phys. 179, 1186–1221 (2020)
5. M. Chantry, H. Christensen, P. Dueben, T. Palmer, Opportunities and challenges for machine learning in weather and climate modelling: Hard, medium and soft AI. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 379(2194), 20200083 (2021). https://doi.org/10.1098/rsta.2020.0083. (Accessed 11 March 2021)
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1. Topical issue on quantitative AI in complex fluids and complex flows: challenges and benchmarks;The European Physical Journal E;2023-10
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