activity
20172026
most citedFinite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier-Stokes equations

221 citations · 1.1k across the 59 of their papers we have counts for

collaborators

65 papers

math.NA2026

A Nonlinear Physics-based Reduced Order Model with Convolutional-based Operator Compression

Anna Ivagnes, Giovanni Stabile, Gianluigi Rozza

Reduced-order models (ROMs) are widely used to accelerate the solution of parametrized partial differential equations, with projection-based methods relying on a linear representat…

math.NA2026

An efficient hyper reduced-order model for segregated solvers for geometrical parametrization problems

Valentin Nkana Ngan, Giovanni Stabile, Andrea Mola +1

We propose an efficient hyper-reduced order model (HROM) designed for segregated finite-volume solvers in geometrically parametrized problems. The method follows a discretize-then-…

physics.flu-dyn2026

Data-driven reduced order model for residence time distribution analysis of an industrial-scale continuous casting tundish

Harshith Gowrachari, Mattia Giuseppe Barra, Giovanni Stabile +2

The continuous casting tundish plays a critical role as a metallurgical reactor in the continuous casting process, with its flow characteristics serving as a key parameter in the p…

math.NA2025

ROM for Viscous, Incompressible Flow in Polygons -- exponential -width bounds and convergence rate

Francesco Romor, Federico Pichi, Giovanni Stabile +2

We demonstrate exponential convergence of Reduced Order Model (ROM) approximations for mixed boundary value problems of the stationary, incompressible Navier-Stokes equations in pl…

physics.flu-dyn2025

Reservoir computing based predictive reduced order model for steel grade intermixing in an industrial continuous casting tundish

Harshith Gowrachari, Mattia Giuseppe Barra, Giovanni Stabile +2

Continuous casting is a widely adopted process in the steel industry, where maintaining high steel quality is paramount. Efficient prediction of grade intermixing during ladle chan…

math.NA2025

Coupling Physics Informed Neural Networks with External Solvers

Rahul Halder, Giovanni Stabile, Gianluigi Rozza

The current work aims to incorporate physics-based loss in Physics Informed Neural Network (PINN) directly using the numerical residual obtained from the governing equation in any…