Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Education and Culture Executive Agency (EACEA). Neither the European Union nor EACEA can be held responsible for them.


Machine Learning for Fluid Mechanics

This review chapter shows how supervised, semi-supervised, and unsupervised learning can be leveraged for fluid mechanics. The review discusses Neural Networks, RNNs, LSTMs, GANs, reinforcement learning, dimensionality reduction, clustering, and more for flow modeling and flow control applications. There is also a discussion of flow optimization in the chapter that covers using stochastic algorithms. The chapter presents a historical context to the information alongside a thorough presentation of the state-of-the-art, and comments on future issues in the field.

Scientific Paper

Machine Learning

Target Group:


Author of the review:
Jake Walker
TU Delft


You have to login to leave a comment. If you are not registered click here