221 citations · 1.1k across the 59 of their papers we have counts for
51 papers · 1 filter
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…
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-…
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…
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…
A new data-driven energy-stable Evolve-Filter-Relax model for turbulent flow simulation
Anna Ivagnes, Toby van Gastelen, Syver Døving Agdestein +3
We present a novel approach to define the filter and relax steps in the evolve-filter-relax (EFR) framework for simulating turbulent flows. The EFR main advantages are its ease of…
Exact operator inference with minimal data
Henrik Rosenberger, Benjamin Sanderse, Giovanni Stabile
This work introduces a novel method to generate snapshot data for operator inference that guarantees the exact reconstruction of intrusive projection-based reduced-order models (RO…
Data-driven Closure Strategies for Parametrized Reduced Order Models via Deep Operator Networks
Anna Ivagnes, Giovanni Stabile, Gianluigi Rozza
In this paper, we propose an equation-based parametric Reduced Order Model (ROM), whose accuracy is improved with data-driven terms added into the reduced equations. These addition…
Model Reduction for Transport-Dominated Problems via Cross-Correlation Based Snapshot Registration
Harshith Gowrachari, Giovanni Stabile, Gianluigi Rozza
Traditional linear approximation methods, such as proper orthogonal decomposition and the reduced basis method, are ill-suited for transport-dominated problems due to the slow deca…
Stochastic Parameter Prediction in Cardiovascular Problems
Kabir Bakhshaei, Sajad Salavatidezfouli, Giovanni Stabile +1
Patient-specific modeling of cardiovascular flows with high-fidelity is challenging due to its dependence on accurately estimated velocity boundary profiles, which are essential fo…
Non-intrusive model reduction of advection-dominated hyperbolic problems using neural network shift augmented manifold transformation
Harshith Gowrachari, Nicola Demo, Giovanni Stabile +1
Advection-dominated problems are predominantly noticed in nature, engineering systems, and various industrial processes. Traditional linear compression methods, such as proper orth…
Parametric Intrusive Reduced Order Models enhanced with Machine Learning Correction Terms
Anna Ivagnes, Giovanni Stabile, Gianluigi Rozza
In this paper, we propose an equation-based parametric Reduced Order Model (ROM), whose accuracy is improved with data-driven terms added into the reduced equations. These addition…
Optimized Bayesian Framework for Inverse Heat Transfer Problems Using Reduced Order Methods
Kabir Bakhshaei, Umberto Emil Morelli, Giovanni Stabile +1
A stochastic inverse heat transfer problem is formulated to infer the transient heat flux, treated as an unknown Neumann boundary condition. Therefore, an Ensemble-based Simultaneo…
A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a Concave Surface
Sajad Salavatidezfouli, Saeid Rakhsha, Armin Sheidani +2
This paper aims to comprehensively investigate the efficacy of various Model Order Reduction (MOR) and deep learning techniques in predicting heat transfer in a pulsed jet impingin…
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…
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…
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…
Assessment of URANS and LES Methods in Predicting Wake Shed Behind a Vertical Axis Wind Turbine
Armin Sheidani, Sajad Salavatidezfouli, Giovanni Stabile +1
In order to shed light on the Vertical-Axis Wind Turbines (VAWT) wake characteristics, in this paper we present high-fidelity CFD simulations of the flow around an exemplary H-shap…