Data-driven reduced-order modeling for nonlinear aerodynamics using an autoencoder neural network
Sign Up to like & getrecommendations! Published in 2024 at "Physics of Fluids"
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
Compressed Feedback Using Autoencoder Based on Deep Learning for D2D Communication Networks
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Wireless Communications Letters"
DOI: 10.1109/lwc.2023.3234574
Abstract: In this letter, we propose a feedback reduction scheme based on AutoEncoder and deep reinforcement learning for frequency division duplex (FDD) overlay device-to-device (D2D) communication networks. All D2D receivers and transmitters are equipped with an… read more here.
Keywords: using autoencoder; autoencoder; communication networks; d2d communication ... See more keywords