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08/26/2023
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
Reduced order models (ROMs) are widely used in scientific computing to t...
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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...
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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...
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08/07/2023
Friedrichs' systems discretized with the Discontinuous Galerkin method: domain decomposable model order reduction and Graph Neural Networks approximating vanishing viscosity so
Friedrichs' systems (FS) are symmetric positive linear systems of first-...
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08/03/2023
An optimisation-based domain-decomposition reduced order model for parameter-dependent non-stationary fluid dynamics problems
In this work, we address parametric non-stationary fluid dynamics proble...
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05/31/2023
Reduced order models for the buckling of hyperelastic beams
In this paper, we discuss reduced order modelling approaches to bifurcat...
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05/25/2023
Generative Adversarial Reduced Order Modelling
In this work, we present GAROM, a new approach for reduced order modelli...
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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...
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05/22/2023
Filter stabilization for the mildly compressible Euler equations with application to atmosphere dynamics simulations
We present a filter stabilization technique for the mildly compressible ...
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05/12/2023
A shape optimization pipeline for marine propellers by means of reduced order modeling techniques
In this paper, we propose a shape optimization pipeline for propeller bl...
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05/08/2023
A physics-based reduced order model for urban air pollution prediction
This article presents an innovative approach for developing an efficient...
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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...
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03/29/2023
Model Order Reduction for Deforming Domain Problems in a Time-Continuous Space-Time Setting
In the context of simulation-based methods, multiple challenges arise, t...
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03/25/2023
Weighted reduced order methods for uncertainty quantification in computational fluid dynamics
In this manuscript we propose and analyze weighted reduced order methods...
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02/24/2023
A DeepONet Multi-Fidelity Approach for Residual Learning in Reduced Order Modeling
In the present work, we introduce a novel approach to enhance the precis...
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02/24/2023
A Non-Intrusive Data-Driven Reduced Order Model for Parametrized CFD-DEM Numerical Simulations
The investigation of fluid-solid systems is very important in a lot of i...
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01/29/2023
Reduced Basis, Embedded Methods and Parametrized Levelset Geometry
In this chapter we examine reduced order techniques for geometrical para...
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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...
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01/05/2023
Stabilized Weighted Reduced Order Methods for Parametrized Advection-Dominated Optimal Control Problems governed by Partial Differential Equations with Random Inputs
In this work, we analyze Parametrized Advection-Dominated distributed Op...
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01/05/2023
A Streamline upwind Petrov-Galerkin Reduced Order Method for Advection-Dominated Partial Differential Equations under Optimal Control
In this paper we will consider distributed Linear-Quadratic Optimal Cont...
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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...
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12/20/2022
Non-intrusive reduced order models for the accurate prediction of bifurcating phenomena in compressible fluid dynamics
The present works is focused on studying bifurcating solutions in compre...
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12/15/2022
Deep learning-based reduced-order methods for fast transient dynamics
In recent years, large-scale numerical simulations played an essential r...
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11/30/2022
A linear filter regularization for POD-based reduced order models of the quasi-geostrophic equations
We propose a regularization for Reduced Order Models (ROMs) of the quasi...
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11/26/2022
An optimisation-based domain-decomposition reduced order model for the incompressible Navier-Stokes equations
The aim of this work is to present a model reduction technique in the fr...
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10/26/2022
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifol
In this work, we propose a model order reduction framework to deal with ...
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10/24/2022
A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
Convolutional Neural Network (CNN) is one of the most important architec...
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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...
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09/13/2022
A unified steady and unsteady formulation for hydrodynamic potential flow simulations with fully nonlinear free surface boundary conditions
This work discusses the correct modeling of the fully nonlinear free sur...
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07/27/2022
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
As a major breakthrough in artificial intelligence and deep learning, Co...
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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...
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06/30/2022
A data-driven Reduced Order Method for parametric optimal blood flow control: application to coronary bypass graft
We consider an optimal flow control problem in a patient-specific corona...
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06/02/2022
A multi-fidelity approach coupling parameter space reduction and non-intrusive POD with application to structural optimization of passenger ship hulls
Nowadays, the shipbuilding industry is facing a radical change towards s...
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05/30/2022
Pressure Data-Driven Variational Multiscale Reduced Order Models
In this paper, we develop data-driven closure/correction terms to increa...
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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 ...
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03/16/2022
An introduction to POD-Greedy-Galerkin reduced basis method
Partial differential equations can be used to model many problems in sev...
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03/01/2022
Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method
Non-affine parametric dependencies, nonlinearities and advection-dominat...
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02/18/2022
Towards a Numerical Proof of Turbulence Closure
The development of turbulence closure models, parametrizing the influenc...
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02/18/2022
Data-Driven Enhanced Model Reduction for Bifurcating Models in Computational Fluid Dynamics
We investigate various data-driven methods to enhance projection-based m...
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02/01/2022
A novel Large Eddy Simulation model for the Quasi-Geostrophic Equations in a Finite Volume setting
We present a Large Eddy Simulation (LES) approach based on a nonlinear d...
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01/26/2022
A Data-Driven Surrogate Modeling Approach for Time-Dependent Incompressible Navier-Stokes Equations with Dynamic Mode Decomposition and Manifold Interpolation
This work introduces a novel approach for data-driven model reduction of...
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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 ...
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01/14/2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that e...
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01/14/2022
Reduced order modeling for spectral element methods: current developments in Nektar++ and further perspectives
In this paper, we present recent efforts to develop reduced order modeli...
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01/10/2022
Projection based semi–implicit partitioned Reduced Basis Method for non parametrized and parametrized Fluid–Structure Interaction problems
The goal of this manuscript is to present a partitioned Model Order Redu...
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01/10/2022
Model Reduction Using Sparse Polynomial Interpolation for the Incompressible Navier-Stokes Equations
This work investigates the use of sparse polynomial interpolation as a m...
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01/05/2022
Fast and accurate numerical simulations for the study of coronary artery bypass grafts by artificial neural network
In this work a machine learning-based Reduced Order Model (ROM) is devel...
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01/03/2022
A POD-Galerkin reduced order model for the Navier-Stokes equations in stream function-vorticity formulation
We develop a Proper Orthogonal Decomposition (POD)-Galerkin based Reduce...
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11/27/2021
A data-driven partitioned approach for the resolution of time-dependent optimal control problems with dynamic mode decomposition
This work recasts time-dependent optimal control problems governed by pa...
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11/16/2021