Three members of the AI for Net Zero presented their recent results on real-time digital twins, reinforcement learning, and wind farm optimisation in the first ERCOFTAC Workshop on Machine Learning for Fluid Dynamics at the Sorbonne University in Paris.
The titles of the talks were:
Real-Time Inference of Model Errors from Experimental Data: Application in Hydrogen-Based Annular Combustors – Andrea Novoa, Nicolas Noiray, James R Dawson and Luca Magri.
Reinforcement Learning of Active Flow Control in Partially Observable Environments – Max Weissenbacher, Anastasia Borovykh, Georgios Rigas.
Multi-fidelity Bayesian Optimisation for the Control of Wind Turbines based on LES and Wake models – Andrew Mole, Sylvain Laizet.