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Stacked Generative Machine Learning Models for Fast Approximations of Steady-State Navier-Stokes Equations
[article]
2021
arXiv
pre-print
Computational fluid dynamics (CFD) simulations are broadly applied in engineering and physics. A standard description of fluid dynamics requires solving the Navier-Stokes (N-S) equations in different flow regimes. However, applications of CFD simulations are computationally-limited by the availability, speed, and parallelism of high-performance computing. To improve computational efficiency, machine learning techniques have been used to create accelerated data-driven approximations for CFD. A
arXiv:2112.06419v1
fatcat:hd4caoonive5dokqz7x2cqqq7u