What is it about?

In fluid dynamics research, one repeatedly encounters unknown quantities that are difficult to measure or simulate. This is especially true when flows are approximated on the basis of time averages. In this case, the influence of turbulent fluctuations must be estimated or modeled. In this study, a new method from the field of scientific machine learning is applied to problems in fluid mechanics, which makes it possible to identify so-called hidden variables by including physical equations in the learning process of neural networks. We apply the method to various fluid mechanics problems and show the great potential of the method for the study of complex phenomena in fluid mechanics.

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Why is it important?

Machine learning methods have become more powerful in recent years due to the increasing availability of computing power. As a result, they are being used more and more frequently in various areas of science. In contrast to classic application areas of machine learning, such as image recognition, comparatively little data is available in fluid dynamics research. To take advantage of the power of these data-driven methods here, it is crucial to incorporate physics-based knowledge into the process. By applying Physics-Informed Neural Networks to mean field data assimilation, we show how data-driven ML methods and physical conservation equations can be combined for cutting-edge research in fluid dynamics.


The combination of modern machine learning methods and classical physics makes this research very exciting. I am sure that such methods will lead to significant new breakthroughs in research in the future!

Moritz Reumschüssel
Technische Universitat Berlin

Read the Original

This page is a summary of: Mean flow data assimilation based on physics-informed neural networks, Physics of Fluids, November 2022, American Institute of Physics,
DOI: 10.1063/5.0116218.
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