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Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equations

Computers & fluids, 2018-09, Vol.173, p.273-284

2018

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Enhancing CFD predictions in shape design problems by model and parameter space reduction

Advanced modeling and simulation in engineering sciences, 2020-10, Vol.7 (1), p.1-19, Article 40

2020

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Large-scale graph-machine-learning surrogate models for 3D-flowfield prediction in external aerodynamics

Advanced modeling and simulation in engineering sciences, 2024-12, Vol.11 (1), p.6-26, Article 6

2024

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Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters

Fluids (Basel), 2021-08, Vol.6 (8), p.296

2021

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The Role of Combined Muscle Ultrasound and Bioimpedentiometry Parameters for Sarcopenia Diagnosis in a Population of Hospitalized Older Adults

Nutrients, 2024-08, Vol.16 (15), p.2429

2024

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Data-driven POD-Galerkin reduced order model for turbulent flows

Journal of computational physics, 2020-09, Vol.416, p.109513, Article 109513

2020

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Efficient geometrical parametrization for finite‐volume‐based reduced order methods

International journal for numerical methods in engineering, 2020-06, Vol.121 (12), p.2655-2682

2020

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Bayesian identification of a projection-based reduced order model for computational fluid dynamics

Computers & fluids, 2020-04, Vol.201, p.104477, Article 104477

2020

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The Neural Network shifted-proper orthogonal decomposition: A machine learning approach for non-linear reduction of hyperbolic equations

Computer methods in applied mechanics and engineering, 2022-03, Vol.392, p.114687, Article 114687

2022

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A reduced order variational multiscale approach for turbulent flows

Advances in computational mathematics, 2019-12, Vol.45 (5-6), p.2349-2368

2019

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Non-linear Manifold Reduced-Order Models with Convolutional Autoencoders and Reduced Over-Collocation Method

Journal of scientific computing, 2023-03, Vol.94 (3), p.74, Article 74

2023

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Preface

Computers & mathematics with applications (1987), 2024-02, Vol.155, p.14

2024

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Model Order Reduction: Volume 2: Snapshot-Based Methods and Algorithms

Benner, Peter

1st edition., 2020

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Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators: Ramses

1, 2024

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Model Order Reduction: Snapshot-Based Methods and Algorithms

2020

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Assessment of URANS and LES methods in predicting wake shed behind a vertical axis wind turbine

Journal of wind engineering and industrial aerodynamics, 2023-01, Vol.232, p.105285, Article 105285

2023

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Generative models for the deformation of industrial shapes with linear geometric constraints: Model order and parameter space reductions

Computer methods in applied mechanics and engineering, 2024-04, Vol.423, p.116823, Article 116823

2024

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Pressure data-driven variational multiscale reduced order models

Journal of computational physics, 2023-03, Vol.476, p.111904, Article 111904

2023

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POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: vortex shedding around a circular cylinder

Communications in Applied and Industrial Mathematics, 2017-12, Vol.8 (1), p.210-236

2017

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Hybrid data-driven closure strategies for reduced order modeling

Applied mathematics and computation, 2023-07, Vol.448, p.127920, Article 127920

2023

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A continuous convolutional trainable filter for modelling unstructured data

Computational mechanics, 2023-08, Vol.72 (2), p.253-265

2023

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A two-stage deep learning architecture for model reduction of parametric time-dependent problems

Computers & mathematics with applications (1987), 2023-11, Vol.149, p.115-127

2023

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A reduced basis approach for PDEs on parametrized geometries based on the shifted boundary finite element method and application to a Stokes flow

Computer methods in applied mechanics and engineering, 2019-04, Vol.347 (C), p.568-587

2019

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Finite volume POD-Galerkin stabilised reduced order methods for the parametrised incompressible Navier–Stokes equations

Enhancing CFD predictions in shape design problems by model and parameter space reduction

Large-scale graph-machine-learning surrogate models for 3D-flowfield prediction in external aerodynamics

Hybrid Neural Network Reduced Order Modelling for Turbulent Flows with Geometric Parameters

The Role of Combined Muscle Ultrasound and Bioimpedentiometry Parameters for Sarcopenia Diagnosis in a Population of Hospitalized Older Adults

Prevalence and predictors of bowel dysfunction in a large multiple sclerosis outpatient population: an Italian multicenter study

Correction to: Prevalence and predictors of bowel dysfunction in a large multiple sclerosis outpatient population: an Italian multicenter study

Data-driven POD-Galerkin reduced order model for turbulent flows

Efficient geometrical parametrization for finite‐volume‐based reduced order methods

Bayesian identification of a projection-based reduced order model for computational fluid dynamics

The Neural Network shifted-proper orthogonal decomposition: A machine learning approach for non-linear reduction of hyperbolic equations

A reduced order variational multiscale approach for turbulent flows

Non-linear Manifold Reduced-Order Models with Convolutional Autoencoders and Reduced Over-Collocation Method

Preface

Model Order Reduction: Volume 2: Snapshot-Based Methods and Algorithms

Reduction, Approximation, Machine Learning, Surrogates, Emulators and Simulators: Ramses

Model Order Reduction: Snapshot-Based Methods and Algorithms

Assessment of URANS and LES methods in predicting wake shed behind a vertical axis wind turbine

Generative models for the deformation of industrial shapes with linear geometric constraints: Model order and parameter space reductions

Pressure data-driven variational multiscale reduced order models

POD-Galerkin reduced order methods for CFD using Finite Volume Discretisation: vortex shedding around a circular cylinder

Hybrid data-driven closure strategies for reduced order modeling

A continuous convolutional trainable filter for modelling unstructured data

A two-stage deep learning architecture for model reduction of parametric time-dependent problems

A reduced basis approach for PDEs on parametrized geometries based on the shifted boundary finite element method and application to a Stokes flow

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