Special issue on machine learning and data-driven methods in fluid dynamics
Author:
Publisher
Springer Science and Business Media LLC
Subject
Fluid Flow and Transfer Processes,General Engineering,Condensed Matter Physics,Computational Mechanics
Link
https://link.springer.com/content/pdf/10.1007/s00162-020-00542-y.pdf
Reference28 articles.
1. https://www.ncdc.noaa.gov/data-access
2. Balachandar, S., Moore, W.C., Akiki, G., Liu, K.: Towards particle-resolved accuracy in Euler-Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation. Theor. Comput. Fluid Dyn. 34(3) (2020). https://doi.org/10.1007/s00162-020-00538-8
3. Bhattacharjee, D., Klose, B., Jacobs, G.B., Hemati, M.S.: Data-driven selection of actuators for optimal control of airfoil separation. Theor. Comput. Fluid Dyn. 34(3) (2020). https://doi.org/10.1007/s00162-020-00526-y
4. Bieker, K., Peitz, S., Brunton, S.L., Kutz, J.N., Dellnitz, M.: Deep model predictive flow control with limited sensor data and online learning. Theor. Comput. Fluid Dyn. 34(3) (2020). https://doi.org/10.1007/s00162-020-00520-4
5. Brenner, M., Eldredge, J., Freund, J.: Perspective on machine learning for advancing fluid mechanics. Phys. Rev. Fluids 4(10), 100501 (2019)
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