activity
20182026
most citedHull shape design optimization with parameter space and model reductions, and self-learning mesh morphing

48 citations · 95 across the 81 of their papers we have counts for

collaborators
Showing 2023Show all

22 papers · 1 filter

math.NA2023

Physics Informed Neural Network Framework for Unsteady Discretized Reduced Order System

Rahul Halder, Giovanni Stabile, Gianluigi Rozza

This work addresses the development of a physics-informed neural network (PINN) with a loss term derived from a discretized time-dependent reduced-order system. In this work, first…

math.NA2023

Modal Analysis of the Wake Shed Behind a Horizontal Axis Wind Turbine with Flexible Blades

Sajad Salavatidezfouli, Armin Sheidani, Kabir Bakhshaei +4

The proper orthogonal decomposition has been applied on a full-scale horizontal-axis wind turbine to shed light on the wake characteristics behind the wind turbine. In reality, the…

math.NA2023

Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jets

Sajad Salavatidezfouli, Giovanni Stabile, Gianluigi Rozza

This research study explores the applicability of Deep Reinforcement Learning (DRL) for thermal control based on Computational Fluid Dynamics. To accomplish that, the forced convec…

physics.flu-dyn2023

A Reduced Order Model formulation for left atrium flow: an Atrial Fibrillation case

Caterina Balzotti, Pierfrancesco Siena, Michele Girfoglio +5

A data-driven Reduced Order Model (ROM) based on a Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) approach is adopted in this paper for the analysis of blood flo…

math.NA2023

Generative Models for the Deformation of Industrial Shapes with Linear Geometric Constraints: model order and parameter space reductions

Guglielmo Padula, Francesco Romor, Giovanni Stabile +1

Real-world applications of computational fluid dynamics often involve the evaluation of quantities of interest for several distinct geometries that define the computational domain…

math.NA20232 cited

Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations

Francesco Romor, Giovanni Stabile, Gianluigi Rozza

A slow decaying Kolmogorov n-width of the solution manifold of a parametric partial differential equation precludes the realization of efficient linear projection-based reduced-ord…