Physics-aware Reduced-order Modeling of Transonic Flow via $\beta$-Variational Autoencoder. (arXiv:2205.00608v2 [physics.flu-dyn] UPDATED)

Autoencoder-based reduced-order modeling (ROM) has recently attracted
significant attention, owing to its ability to capture underlying nonlinear
features. However, two critical drawbacks severely undermine its scalability to
various physical applications: entangled and therefore uninterpretable latent
variables (LVs) and the blindfold determination of latent space dimension. In
this regard, this study proposes the physics-aware ROM using only interpretable
and information-intensive LVs extracted by $\beta$-variational autoencoder,
which are referred to as physics-aware LVs throughout this paper. To extract
these LVs, their independence …

arxiv autoencoder beta flow modeling physics