221 citations · 1.1k across the 59 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
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…
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…
Geometrically Parametrised Reduced Order Models for the Study of Hysteresis of the Coanda Effect in Finite Element-based Incompressible Fluid Dynamics
J. R. Bravo, G. Stabile, M. Hess +3
This article presents a general reduced order model (ROM) framework for addressing fluid dynamics problems involving time-dependent geometric parametrisations. The framework integr…
A physics-based reduced order model for urban air pollution prediction
Moaad Khamlich, Giovanni Stabile, Gianluigi Rozza +2
This article presents an innovative approach for developing an efficient reduced-order model to study the dispersion of urban air pollutants. The need for real-time air quality mon…
A reduced-order model for segregated fluid-structure interaction solvers based on an ALE approach
Valentin Nkana Ngan, Giovanni Stabile, Andrea Mola +1
This article presents a Galerkin projection-based reduced-order modelling (ROM) approach for segregated fluid-structure interaction (FSI) problems, formulated within an Arbitrary L…
Applicable Methodologies for the Mass Transfer Phenomenon in Tumble Dryers: A Review
Sajad Salavatidezfouli, Arash Hajisharifi, Michele Girfoglio +2
Tumble dryers offer a fast and convenient way of drying textiles independent of weather conditions and therefore are frequently used in ordinary households. However, artificial dry…
Reduced Basis, Embedded Methods and Parametrized Levelset Geometry
Efthymios N. Karatzas, Giovanni Stabile, Francesco Ballarin +1
In this chapter we examine reduced order techniques for geometrical parametrized heat exchange systems, Poisson, and flows based on Stokes, steady and unsteady incompressible Navie…
A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems
Isabella Carla Gonnella, Martin W. Hess, Giovanni Stabile +1
Parametric time-dependent systems are of a crucial importance in modeling real phenomena, often characterized by non-linear behaviors too. Those solutions are typically difficult t…