3 papers
physics.flu-dyn2026
Latent-space variational data assimilation in two-dimensional turbulence
Andrew Cleary, Qi Wang, Tamer A. Zaki
Starting from limited measurements of a turbulent flow, data assimilation (DA) attempts to estimate all the spatio-temporal scales of motion. Success is dependent on whether the sy…
physics.flu-dyn2025
Characterizing the Reynolds number dependence of the chaotic attractor in two-dimensional turbulence with dimension-minimizing autoencoders
Andrew Cleary, Jacob Page
Deep autoencoder neural networks can generate highly accurate, low-order representations of turbulence. We design a new family of autoencoders which are a combination of a 'dense-b…
physics.flu-dyn2025
Dynamical relevance of periodic orbits under increasing Reynolds number and connections to inviscid dynamics
Andrew Cleary, Jacob Page
Large numbers of relative periodic orbits (RPOs) have been found recently in doubly-periodic, two-dimensional Kolmogorov flow at moderate Reynolds numbers . Whi…