Data-driven reduced-order modeling for nonlinear aerodynamics using an autoencoder neural network
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DOI: 10.1063/5.0177577
Abstract: The design of commercial air transportation vehicles heavily relies on understanding and modeling fluid flows, which pose computational challenges due to their complexity and high degrees of freedom. To overcome these challenges, we propose a… read more here.
Keywords: using autoencoder; reduced order; autoencoder neural; aerodynamics ... See more keywords