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Turbulence closure modeling with data-driven techniques: Investigation of generalizable deep neural networks
Generalizability of machine-learning (ML) based turbulence closures to accurately predict unseen practical flows remains an important challenge.
Fluid Dynamics Computational Physics Data Analysis, Statistics and Probability
Fourier Spectrum Discrepancies in Deep Network Generated Images
Advancements in deep generative models such as generative adversarial networks and variational autoencoders have resulted in the ability to generate realistic images that are visually indistinguishable from real images, which raises concerns about their potential malicious usage.
