7 citations · 7 across the 7 of their papers we have counts for
8 papers
Ergodicity breaking transition beyond qubits
Tanay Pathak, Abhik Kumar Saha
The local Hilbert space dimension is a fundamental parameter in quantum many-body systems, but its role in controlling the ergodicity-breaking transition remains less explored. We…
Feature-based manifold model of actuated wakes
Alicia Rodríguez-Asensio, Guy Y. Cornejo Maceda, Bernd R. Noack +2
We propose a feature-based reduced-order model to predict the transient dynamics of bluff-body wakes under arbitrary time-varying actuation. Starting point is a control-oriented PO…
Turbulence enhancement of a fan array wind generator using geometric texturing and optimization-based control
Gengshou Cao, Tamir Shaqarin, Zhutao Jiang +5
Fan array wind generators (FAWG) are designed to generate a rich set of turbulent flows reminiscent of those found in natural environments. In this study, we experimentally investi…
Sensor optimization for urban wind estimation with cluster-based probabilistic framework
Yutong Liang, Chang Hou, Guy Y. Cornejo Maceda +8
We propose a physics-informed machine-learned framework for sensor-based flow estimation for drone trajectories in complex urban terrain. The input is a rich set of flow simulation…
rDSM -- A robust Downhill Simplex Method software package for optimization problems in high dimensions
Tianyu Wang, Xiaozhou He, Bernd R. Noack
The Downhill Simplex Method (DSM) is a fast-converging derivative-free optimization technique for nonlinear systems. However, the optimization process is often subject to premature…
Machine-learned flow estimation with sparse data -- exemplified for the rooftop of a UAV vertiport
Chang Hou, Luigi Marra, Guy Y. Cornejo Maceda +9
We propose a physics-informed data-driven framework for urban wind estimation. This framework validates and incorporates the Reynolds number independence for flows under various wo…
Flow control-oriented coherent mode prediction via Grassmann-kNN manifold learning
Hongfu Zhang, Hui Tang, Bernd R. Noack
A data-driven method using Grassmann manifold learning is proposed to identify a low-dimensional actuation manifold for flow-controlled fluid flows. The snapshot flow field are twi…
Actuation manifold from snapshot data
Luigi Marra, Guy Y. Cornejo Maceda, Andrea Meilán-Vila +5
We propose a data-driven methodology to learn a low-dimensional manifold of controlled flows. The starting point is resolving snapshot flow data for a representative ensemble of ac…