Search Results for author:
Found 4 papers, 1 papers with code
Reinforcement learning to maximise wind turbine energy generation
The double deep Q- learning is compared with a classic value iteration reinforcement learning control and both strategies outperform a classic PID control in all environments.
An unsupervised machine-learning-based shock sensor for high-order supersonic flow solvers
We present a novel unsupervised machine-learning sock sensor based on Gaussian Mixture Models (GMMs).
A reinforcement learning strategy for p-adaptation in high order solvers
We discuss the impact of RL-based mesh p-adaptation on computational efficiency and accuracy.
Improving aircraft performance using machine learning: a review
This review covers the new developments in machine learning (ML) that are impacting the multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics (experimental and numerical), aerodynamics, acoustics, combustion and structural health monitoring.
