5 papers · 1 filter
Generative Spatio-temporal GraphNet for Transonic Wing Pressure Distribution Forecasting
Gabriele Immordino, Andrea Vaiuso, Andrea Da Ronch +1
This study presents a framework for predicting unsteady transonic wing pressure distributions, integrating an autoencoder architecture with graph convolutional networks and graph-b…
Drone Acoustic Analysis for Predicting Psychoacoustic Annoyance via Artificial Neural Networks
Andrea Vaiuso, Marcello Righi, Oier Coretti +1
Unmanned Aerial Vehicles (UAVs) have become widely used in various fields and industrial applications thanks to their low operational cost, compact size and wide accessibility. How…
Parametric Nonlinear Volterra Series via Machine Learning: Transonic Aerodynamics
Gabriele Immordino, Andrea Da Ronch, Marcello Righi
This study introduces an approach for modeling unsteady transonic aerodynamics within a parametric space, using Volterra series to capture aerodynamic responses and machine learnin…
Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads
Andrea Vaiuso, Gabriele Immordino, Marcello Righi +1
Multi-fidelity models are becoming more prevalent in engineering, particularly in aerospace, as they combine both the computational efficiency of low-fidelity models with the high…
Predicting Transonic Flowfields in Non-Homogeneous Unstructured Grids Using Autoencoder Graph Convolutional Networks
Gabriele Immordino, Andrea Vaiuso, Andrea Da Ronch +1
This paper focuses on addressing challenges posed by non-homogeneous unstructured grids, commonly used in Computational Fluid Dynamics (CFD). Their prevalence in CFD scenarios has…