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Machine learning accelerated computational fluid dynamics
[article]
2021
arXiv
pre-print
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics. Fluids are well described by the Navier-Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between accuracy and tractability. Here we use end-to-end deep learning to improve approximations inside
arXiv:2102.01010v1
fatcat:rp75forirfe43c5rtifo2qbonm