Machine Learning for Complex Flows: Event highlights

Last month, a workshop brought together experts and young researchers for an inspiring exchange of ideas at the intersection of AI and fluid dynamics. Organized by CYPHER COST Action, ENCODING, and MODELAIR, the event welcomed over 70 participants, including 20 PhD students from Marie Curie projects, for two days of insightful discussions and knowledge-sharing.

Exploring ML in Fluid Mechanics

The workshop featured cutting-edge research on how machine learning is transforming computational fluid dynamics (CFD), including:
✅ AI-driven acceleration of solvers
✅ Reinforcement Learning techniques applied to CFD
✅ Neural encoding for more efficient turbulence data processing
✅ ML-enhanced PIV methods
✅ Graph neural networks for predicting physicochemical properties
✅ Machine learning for flame dynamics analysis

Beyond the presentations, the poster session was a highlight, featuring over 30 projects that showcased new approaches to integrating AI with fluid dynamics. Participants had the chance to engage in deep technical discussions, exchange ideas, and explore potential collaborations.

We are grateful to the outstanding experts who shared their insights, making this event a valuable experience for all. To revisit the key moments of the workshop, check out the speakers’ presentations here.

And make sure to check out the highlights video!

With such a strong foundation, we look forward to driving forward the role of machine learning in fluid mechanics and fostering future collaborations in this exciting field!