46 citations · 99 across the 6 of their papers we have counts for
6 papers
Enhancing lattice kinetic schemes for fluid dynamics with Lattice-Equivariant Neural Networks
Giulio Ortali, Alessandro Gabbana, Imre Atmodimedjo +1
We present a new class of equivariant neural networks, hereby dubbed Lattice-Equivariant Neural Networks (LENNs), designed to satisfy local symmetries of a lattice structure. Our a…
Kinetic data-driven approach to turbulence subgrid modeling
Giulio Ortali, Alessandro Gabbana, Nicola Demo +2
Numerical simulations of turbulent flows are well known to pose extreme computational challenges due to the huge number of dynamical degrees of freedom required to correctly descri…
Extreme statistics and extreme events in dynamical models of turbulence
Xander M. de Wit, Giulio Ortali, Alessandro Corbetta +3
We present a study of the intermittent properties of a shell model of turbulence with unprecedented statistics, about eddy turn over time, achieved thanks to an impleme…
A Numerical Proof of Shell Model Turbulence Closure
Giulio Ortali, Alessandro Corbetta, Gianluigi Rozza +1
The development of turbulence closure models, parametrizing the influence of small non-resolved scales on the dynamics of large resolved ones, is an outstanding theoretical challen…
Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems
Giulio Ortali, Nicola Demo, Gianluigi Rozza
This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogon…
An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques
Nicola Demo, Giulio Ortali, Gianluca Gustin +2
This contribution describes the implementation of a data--driven shape optimization pipeline in a naval architecture application. We adopt reduced order models (ROMs) in order to i…