We propose a self-supervised cluster-based hierarchical reduced-order modelling
methodology to model and analyse the complex dynamics arising from a sequence of …

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

We propose a novel cluster-based reduced-order modelling (CROM) strategy for unsteady
flows. CROM combines the cluster analysis pioneered in Gunzburger's group (Burkardt …

We propose the first least-order Galerkin model of an incompressible flow undergoing two
successive supercritical bifurcations of Hopf and pitchfork type. A key enabler is a mean-field …

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 …

Reduced order models (ROMs) that capture flow dynamics are of interest for decreasing
computational costs for simulation as well as for model-based control approaches. This work …

We present a combined experimental and theoretical study of the primary and secondary
instabilities in a Kolmogorov-like flow. The experiment uses electromagnetic forcing with an …

This work deals with the investigation of bifurcating fluid phenomena using a reduced order
modelling setting aided by artificial neural networks. We discuss the POD-NN approach …

Plane Couette flow at Reynolds number (based on the channel half-height and half the
velocity difference between the top and bottom plates) is investigated with a spatial domain …

This paper investigates the large-scale flow reorientations of Rayleigh–Bénard convection in
a cubic cell using proper orthogonal decomposition (POD) analysis and modelling. A direct …