Topical issue on quantitative AI in complex fluids and complex flows: challenges and benchmarks
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/s10189-023-00356-w.pdf
Reference18 articles.
1. A. Alieva, S. Hoyer, M. Brenner, G. Iaccarino, P. Norgaard, Toward accelerated data-driven Rayleigh–Bénard convection simulations. Eur. Phys. J. E 46(7), 64 (2023)
2. S. Angriman, P. Cobelli, P.D. Mininni, M. Obligado, P. Clark Di Leoni, Assimilation of statistical data into turbulent flows using physics-informed neural networks. Eur. Phys. J. E 46(3), 13 (2023)
3. M. Benjamin, S.P. Domino, G. Iaccarino, Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges. Eur. Phys. J. E 46(7), 55 (2023)
4. M.A. Bucci, O. Semeraro, A. Allauzen, S. Chibbaro, L. Mathelin, Curriculum learning for data-driven modeling of dynamical systems. Eur. Phys. J. E 46(3), 12 (2023)
5. M. Buzzicotti, F. Bonaccorso, Inferring turbulent environments via machine learning. Eur. Phys. J. E 45(12), 102 (2022)
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