@article {2024,
	title = {Kinetic data-driven approach to turbulence subgrid modeling},
	journal = {arXiv preprint arXiv:2403.18466},
	year = {2024},
	author = {Giulio Ortali and Gabbana, Alessandro and Demo, Nicola and Rozza, Gianluigi and Toschi, Federico}
}
@conference {2024,
	title = {Modeling Droplets Dynamics in Emulsions with Graph Neural Networks},
	booktitle = {ICML 2024 AI for Science Workshop},
	year = {2024},
	author = {Giulio Ortali and Toschi, Federico and van de Meent, Jan-Willem}
}
@article {2022,
	title = {Numerical proof of shell model turbulence closure},
	journal = {Physical Review Fluids},
	volume = {7},
	year = {2022},
	pages = {L082401},
	author = {Giulio Ortali and Corbetta, Alessandro and Rozza, Gianluigi and Toschi, Federico}
}
@article {2021,
	title = {An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques},
	journal = {Bolletino dell Unione Matematica Italiana},
	volume = {14},
	year = {2021},
	pages = {211-230},
	abstract = {<p>This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. We adopt reduced order models in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation. The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive{\textemdash}especially dealing with complex industrial geometries{\textemdash}we propose also a dynamic mode decomposition enhancement to reduce the computational cost of a single numerical simulation. The real-time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.</p>},
	doi = {10.1007/s40574-020-00263-4},
	author = {Nicola Demo and Giulio Ortali and Gianluca Gustin and Gianluigi Rozza and Gianpiero Lavini}
}
