On behalf of the Organizing Committee, it is our great pleasure to welcome you to the 1st International Symposium on AI and Fluid Mechanics (AIFLUIDs), to be hosted at the MΑΙCh Conference Centre in Chania, Greece. AIFLUIDs aims to stand out as the premier global event dedicated to AI and data-driven methods applied to fluid mechanics.

Traditionally, fluid mechanics has been explored by experimental, theoretical and traditional computational methods. Recently, there has been a resurgence of data-driven and machine learning methods to provide improved understanding and control of fluid flows. At the same time, novel methods of solving the fluid flow conservation equations based on AI techniques are under development. The symposium will offer a unique forum for exploring the latest advancements of AI for the analysis, modelling, simulation and control of fluid flows. It will foster collaborations by informing the community about collection, description and maintenance of benchmark validation databases for machine learning model training.

The symposium will host keynote presentation by world-leading experts, open scientific contributions and posters. Moreover, sponsors and industry experts will participate and discuss challenges and future perspectives. AIFLUIDs is welcoming scientists, engineers and young researchers, as well as industries and corporations actively engaged to all relevant fields to participate to the event. The conference scientific contributions will showcase the application of these methods in both fundamental fluid flow problems and engineering configurations.

  • AI-enhanced Computational Fluid Dynamics

  • Turbulent flows

  • Data-driven ML surrogate models
  • Non-Newtonian fluids

  • Reduced-order models and dimensionality reduction

  • High-speed aerodynamics

  • Image processing and feature detection

  • Hydrodynamics and propulsion

  • Digital twins and real-time simulations

  • Multi-phase and multi-physics flows

  • Single and multi-discipline optimization
  • Flow control and reinforcement learning

  • Uncertainty quantification

  • Process engineering and equations of state

  • Signal processing

  • Fluid-Solid interactions