15 citations · 18 across the 6 of their papers we have counts for
6 papers · 1 filter
Deep reinforcement learning for separation control in turbulent wind-tunnel flow
Sofia Avdiiv, Andre Weiner, Ben Steinfurth
This work investigates Deep Reinforcement Learning (DRL) as a tool for model-free closed-loop active separation control in a fully turbulent wind tunnel flow over a one-sided diffu…
Modal Analysis of Buffet Effects Induced by Ultrahigh Bypass Ratio Nacelle Installation
Sebastian Spinner, Andre Weiner
Unsteady shock-boundary-layer interaction on the lower surface of a transport-aircraft wing can be caused or amplified by ultrahigh-bypass-ratio underwing nacelle installation. Thi…
An octree-based sampling algorithm for analyzing big simulation data
Janis Geise, Sebastian Spinner, Richard Semaan +1
As computational resources continue to increase, the storage and analysis of vast amounts of data will inevitably become a bottleneck in computational fluid dynamics (CFD) and rela…
Dynamic mode decomposition of noisy flow data
Andre Weiner, Janis Geise
Dynamic mode decomposition (DMD) is a popular approach to analyzing and modeling fluid flows. In practice, datasets are almost always corrupted to some degree by noise. The vanilla…
Model-based deep reinforcement learning for accelerated learning from flow simulations
Andre Weiner, Janis Geise
In recent years, deep reinforcement learning has emerged as a technique to solve closed-loop flow control problems. Employing simulation-based environments in reinforcement learnin…
Backpropagation and gradient descent for an optimized dynamic mode decomposition
Andre Weiner, Richard Semaan
We present a robust and flexible optimization approach for dynamic mode decomposition analysis of data with complex dynamics and low signal-to-noise ratios. The approach borrows te…