research
∙
08/07/2023
Generative Models for the Deformation of Industrial Shapes with Linear Geometric Constraints: model order and parameter space reductions
Real-world applications of computational fluid dynamics often involve th...
share
research
∙
08/07/2023
Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations
A slow decaying Kolmogorov n-width of the solution manifold of a paramet...
research
∙
05/23/2023
A reduced-order model for segregated fluid-structure interaction solvers based on an ALE approach
This article presents a Galerkin projection model order reduction approa...
research
∙
05/08/2023
A physics-based reduced order model for urban air pollution prediction
This article presents an innovative approach for developing an efficient...
research
∙
04/28/2023
A novel reduced-order model for advection-dominated problems based on Radon-Cumulative-Distribution Transform
Problems with dominant advection, discontinuities, travelling features, ...
research
∙
04/07/2023
Applicable Methodologies for the Mass Transfer Phenomenon in Tumble Dryers: A Review
Tumble dryers offer a fast and convenient way of drying textiles indepen...
research
∙
01/29/2023
Reduced Basis, Embedded Methods and Parametrized Levelset Geometry
In this chapter we examine reduced order techniques for geometrical para...
research
∙
01/24/2023
A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems
Parametric time-dependent systems are of a crucial importance in modelin...
research
∙
12/26/2022
Assessment of URANS and LES Methods in Predicting Wake Shed Behind a Vertical Axis Wind Turbine
In order to shed light on the Vertical-Axis Wind Turbines (VAWT) wake ch...
research
∙
12/15/2022
Deep learning-based reduced-order methods for fast transient dynamics
In recent years, large-scale numerical simulations played an essential r...
research
∙
10/24/2022
A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
Convolutional Neural Network (CNN) is one of the most important architec...
research
∙
10/05/2022
Novel Methodologies for Solving the Inverse Unsteady Heat Transfer Problem of Estimating the Boundary Heat Flux in Continuous Casting Molds
In this work, we investigate the estimation of the transient mold-slab h...
research
∙
07/21/2022
Hybrid Data-Driven Closure Strategies for Reduced Order Modeling
In this paper, we propose hybrid data-driven ROM closures for fluid flow...
research
∙
05/30/2022
Pressure Data-Driven Variational Multiscale Reduced Order Models
In this paper, we develop data-driven closure/correction terms to increa...
research
∙
05/19/2022
A segregated reduced order model of a pressure-based solver for turbulent compressible flows
This article provides a reduced order modelling framework for turbulent ...
research
∙
03/01/2022
Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method
Non-affine parametric dependencies, nonlinearities and advection-dominat...
research
∙
01/24/2022
Embedded domain Reduced Basis Models for the shallow water hyperbolic equations with the Shifted Boundary Method
We consider fully discrete embedded finite element approximations for a ...
research
∙
10/22/2021
An efficient FV-based Virtual Boundary Method for the simulation of fluid-solid interaction
In this work, the Immersed Boundary Method (IBM) with feedback forcing i...
research
∙
08/14/2021
The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equations
Models with dominant advection always posed a difficult challenge for pr...
research
∙
07/20/2021
Hybrid neural network reduced order modelling for turbulent flows with geometric parameters
Geometrically parametrized Partial Differential Equations are nowadays w...
research
∙
01/28/2021
A numerical approach for heat flux estimation in thin slabs continuous casting molds using data assimilation
In the present work, we consider the industrial problem of estimating in...
research
∙
06/25/2020
On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis
In this work, Dynamic Mode Decomposition (DMD) and Proper Orthogonal Dec...
research
∙
01/15/2020
Enhancing CFD predictions in shape design problems by model and parameter space reduction
In this work we present an advanced computational pipeline for the appro...
research
∙
12/02/2019
Extension and comparison of techniques to enforce boundary conditions in Finite Volume POD-Galerkin reduced order models for fluid dynamic problems
A Finite-Volume based POD-Galerkin reduced order model is developed for ...
research
∙
11/20/2019
Basic Ideas and Tools for Projection-Based Model Reduction of Parametric Partial Differential Equations
We provide first the functional analysis background required for reduced...
research
∙
10/25/2019
Bayesian identification of a projection-based Reduced Order Model for Computational Fluid Dynamics
In this paper we propose a Bayesian method as a numerical way to correct...
research
∙
10/25/2019
Bayesian identification of a projection-based Reduced Order Model for Computational Fluid Dynamics Computers and Fluids
In this paper we propose a Bayesian method as a numerical way to correct...
research
∙
07/23/2019
Data-Driven POD-Galerkin Reduced Order Model for Turbulent Flows
In this work we present a Reduced Order Model which is specifically desi...
research
∙
07/23/2019
A Reduced-Order Shifted Boundary Method for Parametrized incompressible Navier-Stokes equations
We investigate a projection-based reduced-order model of the steady inco...
research
∙
06/20/2019
A Hybrid Reduced Order Method for Modelling Turbulent Heat Transfer Problems
A parametric, hybrid reduced order model approach based on the Proper Or...
research
∙
02/26/2018
