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20212026
most citedNon-intrusive reduced order models for the accurate prediction of bifurcating phenomena in compressible fluid dynamics

1 citations · 2 across the 11 of their papers we have counts for

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5 papers · 1 filter

math.NA2026

A novel compact scheme for second-order fluxes applied to the Spectral Difference method

Guido Lodato, Niccolò Tonicello

The discretization of second-order (viscous) terms in Discontinuous Spectral Element Methods (DSEMs) typically relies on an auxiliary gradient variable, whose treatment at element…

math.NA2026

Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry

G. Li Causi, N. Tonicello, L. Magri +1

Autoencoders (AEs) have emerged as powerful tools for non-linear dimensionality reduction, often surpassing traditional linear methods such as Proper Orthogonal Decomposition (POD)…

physics.flu-dyn2026

End-to-end optimization of subgrid scale models for discontinuous spectral element schemes based on the discrete adjoint method

Nicola Clinco, Niccolò Tonicello, Paola Cinnella +1

In computational fluid dynamics, Large Eddy Simulation (LES) offers a compelling balance between accuracy and computational cost by resolving large-scale flow structures while mode…

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…