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von Karman Institute for Fluid Dynamics | Universit� libre de Bruxelles (ULB)

The integration of machine learning methods in fluid mechanics continues to grow and open new frontiers. Combining first principles and machine learning offers new avenues to improve predictions and control design, identify patterns, reduce computational costs, enhance measurement techniques, and more generally, get deeper insights into complex fluid phenomena. This lecture series provides an overview of the current state of the art and a pedagogical introduction to the fundamentals of machine learning tools for fluid dynamics. The course is in its second edition. It follows the successful lecture series held in 2020, which attracted more than 200 participants and resulted in an edited book published by Cambridge University Press.

Formation continue (en general),    Thermodynamique, dynamique des fluides et physique statistique

von Karman Institute for Fluid Dynamics

This course gives an overview and practical hands-on experience in integrating machine learning in fluid dynamics. The course originated as a compressed version of the course Machine Learning for Fluid Dynamics, given at the Research Master program at the von Karman Institute. After a brief review of the machine learning landscape, we show how to frame problems in fluid mechanics as machine learning problems, and we explore challenges and opportunities. Attendees will be guided through a series of tutorial sessions in Python and will tackle several relevant applications: aeroacoustics' noise prediction, turbulence modelling, reduced-order modelling and forecasting, meshless integration of (partial) differential equations, super-resolution and flow control.

Formation continue (en general),    Thermodynamique, dynamique des fluides et physique statistique