21 citations · 28 across the 9 of their papers we have counts for
9 papers
Policy-DRIFT: Dynamic Reward-Informed Flow Trajectory Steering
Atharva Mahajan, Abhijeet Vishwasrao, Yuning Wang +1
Skin-friction drag induced by wall-bounded turbulent flows accounts for a substantial fraction of energy consumption across commercial aerospace, wind energy, and marine transport.…
Physics-guided surrogate learning enables zero-shot control of turbulent wings
Yuning Wang, Pol Suarez, Mathis Bode +1
Turbulent boundary layers over aerodynamic surfaces are a major source of aircraft drag, yet their control remains challenging due to multiscale dynamics and spatial variability, p…
The HydroGym Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, Sajeda Mokbel, Miro Gondrum +18
Modeling and controlling fluids is critical across science and engineering. Effective flow control can increase lift, reduce drag, enhance mixing, and attenuate noise, potentially…
SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
Maochao Xiao, Yuning Wang, Felix Rodach +15
Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and disco…
Separation control applied to the turbulent flow around a NACA4412 wing section
Yuning Wang, Fermin Mallor, Carlos Guardiola +2
We carried out high-resolution large-eddy simulations (LESs) to investigate the effects of several separation-control approaches on a NACA4412 wing section with spanwise width of $…
Opposition control applied to turbulent wings
Yuning Wang, Marco Atzori, Ricardo Vinuesa
We conducted high-resolution large-eddy simulations (LESs) to explore the effects of opposition control (OC) on turbulent boundary layers (TBLs) over a wing at a chord-based Reynol…
Easy attention: A simple attention mechanism for temporal predictions with transformers
Marcial Sanchis-Agudo, Yuning Wang, Roger Arnau +4
To improve the robustness of transformer neural networks used for temporal-dynamics prediction of chaotic systems, we propose a novel attention mechanism called easy attention whic…
-Variational autoencoders and transformers for reduced-order modelling of fluid flows
Alberto Solera-Rico, Carlos Sanmiguel Vila, M. A. Gómez +4
Variational autoencoder (VAE) architectures have the potential to develop reduced-order models (ROMs) for chaotic fluid flows. We propose a method for learning compact and near-ort…
Physics-informed deep-learning applications to experimental fluid mechanics
Hamidreza Eivazi, Yuning Wang, Ricardo Vinuesa
High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics,…