Most recent SmartAQnet results were presented at the International Workshop on Data-driven Modeling and Optimization in Fluid Mechanics

The event focused on the application of artificial intelligence, machine learning, deep learning, evolutional algorithms and adjoint-based optimization to fluid dynamics-related problems with special focus on turbulent flows and flow control.

In particular, the event aimed to

  • provide an opportunity for young researchers to update their knowledge on the application of data-driven methods and be inspired with new ideas
  • establish a forum for exchange of ideas between the experts using these methods in different applications
  • initiate collaboration and strengthen interdisciplinary research.

Dr. Johannes Riesterer gave a presentation on Gaussian process regression for heterogeneous measuring networks of environmental data.