89 citations · 91 across the 9 of their papers we have counts for
9 papers
Efficient Active Flow Control Strategy for Confined Square Cylinder Wake Using Deep Learning-Based Surrogate Model and Reinforcement Learning
Meng Zhang, Mustafa Z. Yousif, Minze Xu +3
This study presents a deep learning model-based reinforcement learning (DL-MBRL) approach for active control of two-dimensional (2D) wake flow past a square cylinder using antiphas…
Self-Supervised Learning for Effective Denoising of Flow Fields
Linqi Yu, Mustafa Z. Yousif, Dan Zhou +3
In this study, we proposed an efficient approach based on a deep learning (DL) denoising autoencoder (DAE) model for denoising noisy flow fields. The DAE operates on a self-learnin…
Flow Reconstruction Using Spatially Restricted Domains Based on Enhanced Super-Resolution Generative Adversarial Networks
Mustafa Z. Yousif, Dan Zhou, Linqi Yu +4
This study aims to reconstruct the complete flow field from spatially restricted domain data by utilizing an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model…
Optimizing Flow Control with Deep Reinforcement Learning: Plasma Actuator Placement around a Square Cylinder
Mustafa Z Yousif, Kolesova Paraskovia, Yifang Yang +5
The present study proposes an active flow control (AFC) approach based on deep reinforcement learning (DRL) to optimize the performance of multiple plasma actuators on a square cyl…
A Swin-Transformer-based Model for Efficient Compression of Turbulent Flow Data
Meng Zhang, Mustafa Z Yousif, Linqi Yu +1
This study proposes a novel deep-learning-based method for generating reduced representations of turbulent flows that ensures efficient storage and transfer while maintaining high…
Active flow control over a finite wall-mounted square cylinder by using multiple plasma actuators
Mustafa Z. Yousif, Yifan Yang, Haifeng Zhou +3
The present study aims to investigate the effectiveness of plasma actuators in controlling the flow around a finite wall-mounted square cylinder (FWMSC) with a longitudinal aspect…
Predicting unavailable parameters from existing velocity fields of turbulent flows using a GAN-based model
Linqi Yu, Mustafa Z. Yousif, Young-Woo Lee +4
In this study, an efficient deep-learning model is developed to predict unavailable parameters, e.g., streamwise velocity, temperature, and pressure from available velocity compone…
Physics-guided deep reinforcement learning for flow field denoising
Mustafa Z. Yousif, Meng Zhang, Yifan Yang +3
A multi-agent deep reinforcement learning (DRL)-based model is presented in this study to reconstruct flow fields from noisy data. A combination of the reinforcement learning with…
A transformer-based synthetic-inflow generator for spatially-developing turbulent boundary layers
Mustafa Z. Yousif, Meng Zhang, Linqi Yu +2
This study proposes a newly-developed deep-learning-based method to generate turbulent inflow conditions for spatially-developing turbulent boundary layer (TBL) simulations. A comb…