The project is attacking important issues connected to Lagrangian and Eulerian data-driven problems for complex flows, with particular interests to applications to geophysical and laboratory set-ups. We have started to attach single-probe self-propelled objects with specific point-to-point navigation targets in complex flows, and multi-probe cooperative or adversarial games in stochastic and/or turbulent flow Field-taxis based on optimal and heuristic search for odor emissions in stochastic models and in realistic numerical simulations of turbulence with a mean wind have also been attacked. Among the various techniques developed we cite the ones based on Reinforcement learning, Adversarial Reinforcement Learning and optimal control.
Data assimilation problems for rotating turbulent flows and lagrangian turbulent trajectories have been also developed by using both equations-based and data-driven tools, including linear methods based on principal orthogonal decomposition and non-linear ones, based on convolutional neural networks, Generative Adversarial Networks and, more recently, Diffusion Models. Comparison against Nudging is also systematically performed. Explorative studies of complex multi-component and multi-phase flows, using Lattice Boltzmann Techniques, and development of new algorithm to study nucleation and thermal flows with mesoscopic approaches are also under investigation. Among the most important impact for the society we cite the possibility to better assimilate data for numerical weather forecast, develop augmented reality approaches for bio-fluids and medicine, developing of optimal strategies for odor and contaminant sources in the presence of strong flow fluctuations. The overall objectives are to make a quantitative jump to data-driven approaches for fluid dynamics, offering open data suites to help the community for developing benchmarks and grand challenges,
