most citedTowards human-interpretable, automated learning of feedback control for the mixing layer

2 citations · 2 across the 1 of their papers we have counts for

collaborators

3 papers

physics.flu-dyn2020

Galerkin force model for transient and post-transient dynamics of the fluidic pinbal

Nan Deng, Bernd R. Noack, Marek Morzyński +1

We propose an aerodynamic force model associated with a Galerkin model for the unforced fluidic pinball, the two-dimensional flow around three equal cylinders with one radius dista…

physics.flu-dyn2020★ 2 cited

Towards human-interpretable, automated learning of feedback control for the mixing layer

Hao Li, Guy Y. Cornejo Maceda, Yiqing Li +3

We propose an automated analysis of the flow control behaviour from an ensemble of control laws and associated time-resolved flow snapshots. The input may be the rich data base of…

physics.flu-dyn2020

Cluster-based network model

Hao Li, Daniel Fernex, Richard Semaan +3

We propose an automatable data-driven methodology for robust nonlinear reduced-order modelling from time-resolved snapshot data. In the kinematical coarse-graining, the snapshots a…