147 citations · 323 across the 10 of their papers we have counts for
21 papers · 1 filter
Machine learning flow control with few sensor feedback and measurement noise
R. Castellanos, G. Y. Cornejo Maceda, I. de la Fuente +3
A comparative assessment of machine learning (ML) methods for active flow control is performed. The chosen benchmark problem is the drag reduction of a two-dimensional Kármán vorte…
On closures for reduced order models A spectrum of first-principle to machine-learned avenues
Shady E. Ahmed, Suraj Pawar, Omer San +3
For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr-Sommer…
Route to Chaos in the Fluidic Pinball
Nan Deng, Luc R. Pastur, Marek Morzyński +1
The fluidic pinball has been recently proposed as an attractive and effective flow configuration for exploring machine learning fluid flow control. In this contribution, we focus o…
Low-dimensional Flow Models from high-dimensional Flow data with Machine Learning and First Principles
Nan Deng, Luc R. Pastur, Bernd R. Noack
Reduced-order modelling and system identification can help us figure out the elementary degrees of freedom and the underlying mechanisms from the high-dimensional and nonlinear dyn…
Reduced-order modeling of the fluidic pinball
Luc R. Pastur, Nan Deng, Marek Morzyński +1
The fluidic pinball is a geometrically simple flow configuration with three rotating cylinders on the vertex of an equilateral triangle. Yet, it remains physically rich enough to h…
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…