We study the applicability of artificial intelligence tools to different problems in fluid dynamics, from the search of an optimal navigation strategy in complex environments to data reconstruction from partial measurements of turbulent flows. To solve navigation problems we follow the Reinforcement Learning approach. Here, we focus on finding the path that minimizes the navigation time between two given points in a fluid flow, known as the Zermelo’s problem . Concerning data-assimilation, we explore the capability of Generative Adversarial Network (GAN) to generate missing data in turbulent configurations. In particular, we investigate on a quantitative basis, their use in reconstructing 2d damaged snapshots extracted from a large database of numerical configurations of 3d turbulence in the presence of rotation, a case with multi-scale random features where both large-scale organized structures and small-scale highly intermittent and non- Gaussian fluctuations are present [2,3].
 Biferale, L., Bonaccorso, F., Buzzicotti, M., Di Leoni, P. C., & Gustavsson, K. (2019). Zermelo’s problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning. Chaos: An Interdisciplinary Journal of Nonlinear Science 29.10 (2019): 103138.
 Buzzicotti, M., Bonaccorso, F., Di Leoni, P. C., & Biferale, L. (2020). Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database. arXiv preprint arXiv:2006.09179 (in press Physycal Review FLuids)
 Biferale, L., Bonaccorso, F., Buzzicotti, M. and di Leoni, P.C., 2020. TURB-Rot. A large database of 3d and 2d snapshots from turbulent rotating flows. arXiv:2006.07469.