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20202026
most citedHull shape design optimization with parameter space and model reductions, and self-learning mesh morphing

48 citations · 48 across the 4 of their papers we have counts for

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6 papers · 1 filter

math.NA2026

Constraint-driven Optimization and Parametrization of Industrial NURBS Geometries via Neural Deformation Field

Federico Tamburlin, Giovanni Canali, Giuseppe Alessio D'Inverno +3

This work presents a differentiable framework for the parametrization and shape optimization of industrial CAD geometries represented by multi-patch NURBS surfaces. The method enab…

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-…

math.NA2021

An efficient FV-based Virtual Boundary Method for the simulation of fluid-solid interaction

Michele Girfoglio, Giovanni Stabile, Andrea Mola +1

In this work, the Immersed Boundary Method (IBM) with feedback forcing introduced by Goldstein et al. (1993) and often referred in the literature as the Virtual Boundary Method (VB…

math.NA202148 cited

Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing

Nicola Demo, Marco Tezzele, Andrea Mola +1

In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a…

math.NA2020

On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis

Mahmoud Gadalla, Marta Cianferra, Marco Tezzele +3

In this work, Dynamic Mode Decomposition (DMD) and Proper Orthogonal Decomposition (POD) methodologies are applied to hydroacoustic dataset computed using Large Eddy Simulation (LE…

math.NA2020

Enhancing CFD predictions in shape design problems by model and parameter space reduction

Marco Tezzele, Nicola Demo, Giovanni Stabile +2

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced…