Now in its 6th edition, the Hands-on Machine Learning for Fluid Dynamics course has established itself as a leading training program in the field, with over 500 participants to date from academia, research institutes, and industry. This popular one-week intensive course offers a practical and accessible introduction to machine learning techniques, specifically designed for applications in fluid dynamics.

The course blends concise theoretical lectures with hands-on Python exercises, guiding participants to frame and solve fluid mechanics problems using modern data-driven tools. Topics include regression and uncertainty quantification, dimensionality reduction, reduced-order modeling, turbulence modeling, system identification, data assimilation and inverse problems for digital twinning, reinforcement learning and flow control. Each module is structured to promote active learning, combining expert instruction with interactive coding tutorials that emphasize intuition and real-world relevance.

The course is intended for graduate students, researchers, and professionals with a basic background in Python programming and fluid mechanics. A detailed program for the 6th edition is currently being finalized and will be announced soon.