activity
20122023
most citedMachine Learning for Fluid Mechanics

2.9k citations · 5.6k across the 52 of their papers we have counts for

collaborators

55 papers

physics.flu-dyn2023★ 1 cited

Aerodynamic Characterization of a Fan Array Wind Generator

Songqi Li, Yutong Liu, Zhutao Jiang +3

Experimental assessment of safe and precise flight control algorithms for unmanned aerial vehicles (UAVs) under gusty wind conditions requires the capability to generate a large ra…

cs.LG2023★ 64 cited

Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing

Qiulei Wang, Lei Yan, Gang Hu +3

This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting lower drag and lower lift fluctuations with the additional challenge of sparse sensor…

physics.flu-dyn2023★ 14 cited

Topologically assisted optimization for rotor design

Tianyu Wang, Yannian Yang, Xuanwu Chen +4

We develop and apply a novel shape optimization exemplified for a two-blade rotor with respect to the figure of merit (). This topologically assisted optimization (TAO) contain…

physics.flu-dyn2022★ 2 cited

xMLC -- A Toolkit for Machine Learning Control

Guy Y. Cornejo Maceda, François Lusseyran, Bernd R. Noack

xMLC is the second book of this `Machine Learning Tools in Fluid Mechanics' Series and focuses on Machine Learning Control (MLC). The objectives of this book are two-fold: First, p…

cs.NE2022

A fast converging particle swarm optimization through targeted, position-mutated, elitism (PSO-TPME)

Tamir Shaqarin, Bernd R. Noack

We dramatically improve convergence speed and global exploration capabilities of particle swarm optimization (PSO) through a targeted position-mutated elitism (PSO-TPME). The three…

physics.flu-dyn2022★ 23 cited

Trajectory-optimized cluster-based network model for the sphere wake

Chang Hou, Nan Deng, Bernd R. Noack

We propose a novel trajectory-optimized Cluster-based Network Model (tCNM) for nonlinear model order reduction from time-resolved data following Li et al. ["Cluster-based network m…