EPJ E Topical Issue: Quantitative AI in Complex Fluids and Complex Flows: Challenges and Benchmarks

Guest Editors: Luca Biferale, Michele Buzzicotti and Massimo Cencini.

The collection addresses open problems, challenges, and benchmarks for data-driven and equation-informed tools for data assimilation, prediction, (subgrid-scale) modeling, inpainting, classification, and (optimal) control of Eulerian and Lagrangian problems in complex flows.

The goal is to move from proof-of-concept to quantitative benchmarks and grand challenges, including scaling of algorithms and complexity of datasets.

The original research papers, presented in a colloquium format, focus on the latest experimental, theoretical, or computational advances and address the interpretability, superiority, and usability of data-driven tools when applied to realistic fluid dynamics problems in engineering, geophysics, biophysics, and other fields. Key topics covered include: (i) Modeling and controlling complex flows with data-driven methods. (ii) Prediction and data-assimilation of multiscale flows. (iii) Reconstruction, super-resolution of fluid flows with data-driven and physics-informed tools. (iv) Optimization of navigation and other tasks in complex flows. (v) Animal behavior in flows.

All articles of this collection are available here and are freely accessible until 27 December 2023. For further information read the Editorial.

Open calls for papers