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physics.flu-dyn2026
Physically Organized Latent Spaces in Unsupervised Autoencoders: Evidence from Aerodynamic Databases
Renato Tognaccini, Ettore Saetta, Gianluca Iaccarino
Using aerodynamic databases of progressively increasing complexity - from linear analytical theory to nonlinear separated RANS flows - we demonstrate that an autoencoder, trained s…
physics.flu-dyn2022★ 29 cited
Machine Learning to Predict Aerodynamic Stall
Ettore Saetta, Renato Tognaccini, Gianluca Iaccarino
A convolutional autoencoder is trained using a database of airfoil aerodynamic simulations and assessed in terms of overall accuracy and interpretability. The goal is to predict th…