Data-Driven Turbulence Modelling Challenge

Building on NASA's successful 2022 turbulence modelling challenge, this initiative focuses on advancing data-driven turbulence modelling approaches for 3D cases of industrial relevance. Participants will develop and test novel RANS models, enhanced turbulence closures, hybrid RANS-LES approaches, or other data-driven turbulence modeling techniques on a set of challenging test cases spanning various flow phenomena such as periodic hills, smooth-body separation, automotive wake flows, and wing-body junction flows. Evaluation is based on accuracy, computational cost, and generalizability. The community-driven challenge welcomes test case suggestions from participants. Visit the dedicated challenge webpage at github.com/rmcconke/ml-turbulence-benchmark for complete technical specifications, final test case details, training datasets, boundary conditions, and evaluation protocols.

Organizers: Tyler Buchanan, Paola Cinnella, Richard Dwight, and Ryley McConkey
To participate: Send an expression of interest to t.s.b.buchanan@tudelft.nl

FluidsBench

FluidsBench is a benchmark for Computational Fluid Dynamics (CFD) surrogates, designed to accelerate progress in the development of foundational AI models for fluids. Motivated by similar efforts in weather (WeatherBench 2) and early work on task specific efforts (CarBench), FluidsBench consists of an open-source evaluation framework, training and ground truth data available via external model hubs (e.g., HuggingFace), and a continuously updated website hosting the latest metrics and state-of-the-art leaderboards that will allow for testing of AI surrogate models. In-person and virtual workshops will be held (subject to acceptance) at popular fluids and ML events (e.g NeurIPS, ICML, ML4Fluids) to discuss the latest work and get community direction for this benchmarking effort. Please visit the website to signup to the mailing list to be notified when it'll be ready for submissions.

Organizers: Neil Ashton, Paola Cinnella, Richard Dwight, Astrid Walle, Ricardo Vineusa, Jean Kossaifi, Daniel Leibovici
To participate: visit fluidsbench.org to sign up to the mailing list and/or e-mail admin@fluidsbench.org

CYPHER Data Challenge: Machine-Learning-Enhanced Turbulent Combustion Closures for LES

The CYPHER data challenge focuses on benchmarking machine-learning approaches for modelling sub-filter closure terms in Large Eddy Simulation (LES) of turbulent lean premixed hydrogen flames. Using high-fidelity Direct Numerical Simulation (DNS) datasets, participants will develop and evaluate machine-learning models to predict unresolved quantities arising from turbulence–chemistry interaction, such as the sub-filter scalar flux of the filtered progress variable. The challenge aims to promote reproducible comparison of different model architectures while encouraging solutions that balance predictive accuracy and computational efficiency. Training and test datasets are derived from DNS of lean premixed hydrogen flames filtered at multiple resolutions. Model evaluation is performed automatically on the Codabench platform and considers both prediction accuracy and inference cost. The challenge is part of the CYPHER COST Action, which promotes collaboration between researchers and industry to accelerate the development of digital tools for renewable-fuel combustion technologies. Visit the dedicated challenge repository for datasets, problem specifications, and submission instructions.

Organizers: Pasquale Lapenna, Lorenzo Piu, Antonio Attili, Alessandro Parente, Paola Cinnella, Federica Ferraro, Anh Khoa Doan
Slides and links: apriori.readthedocs.io/en/latest/conferences/ML4Fluids2026.html
To participate (1st part): codabench.org/competitions/9173
To participate (2nd part): Send an expression of interest to pasquale.lapenna@uniroma1.it