3 papers
math.NA2026
A Multi-Fidelity Parametric Framework for Reduced-Order Modeling using Optimal Transport-based Interpolation: Applications to Diffused-Interface Two-Phase Flows
Moaad Khamlich, Niccolò Tonicello, Federico Pichi +1
This work introduces a data-driven, non-intrusive reduced-order modeling (ROM) framework that leverages Optimal Transport (OT) for multi-fidelity and parametric problems in two-pha…
math.NA2026
Efficient and Accurate Surrogate Modeling of Turbulent Flows via Space-Dependent Aggregation and Reduced Order Models
Piero Zappi, Anna Ivagnes, Niccolò Tonicello +1
Reynolds-Averaged Navier-Stokes (RANS) models are widely used for turbulent flow simulations due to their computational efficiency, but their accuracy strongly depends on the selec…
physics.flu-dyn2024
A data-driven study on Implicit LES using a spectral difference method
Nicola Clinco, Niccolò Tonicello, Gianluigi Rozza
In this paper, we introduce a data-driven filter to analyze the relationship between Implicit Large-Eddy Simulations (ILES) and Direct Numerical Simulations (DNS) in the context of…