PYDA-FDM - Physical and data-driven fluid dynamics modelling

I EDITION | ON SITE | APPLICATION

Deadline for Registration 
8th march, 2027

Period
10th - 14th May, 2027

Learning objectives 

The Seasonal School will introduce participants to the advanced application of Artificial Intelligence and Machine Learning methods for fluid dynamics, a topic of growing relevance for addressing complex scientific and engineering challenges in atmospheric, environmental, aerospace and industrial contexts. The course will provide participants with a structured overview of data-driven, reduced-order and physics-informed approaches for modelling, simulation and prediction of fluid flows, highlighting both their potential and their limitations when applied to real-world problems.

Teaching methodologies

Students will find an interactive and cross-disciplinary learning environment that combines lectures, discussion of case studies and group-based activities. By integrating theoretical foundations with the analysis of real-world applications, the Seasonal School will encourage active participation and critical thinking. The teaching approach is inspired by Problem Based Learning, fostering collaboration among participants and interaction with lecturers in order to develop a deeper understanding of AI-based methods for fluid dynamics.

Target participants

Undergraduate, postgraduate and PhD students from different backgrounds (e.g. engineering, physics, mathematics and data science) who are interested in the development and application of Artificial Intelligence methods for fluid dynamics, combining theoretical concepts with the discussion of case studies across environmental, industrial and aerospace applications.

Coordinator and key teaching staff

Coordinator: Prof. Francesco Montomoli.

key teaching staff: Prof. Giovanni Stabile, Prof. Roberto Buizza, Prof. Tommaso Andreussi, Researcher Vittorio Giannetti

SDGs (https://sdgs.un.org/goals)

(9) Industry, innovation and infrastructure

(12) Responsible consumption and production

(13)  Climate action.