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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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05/12/2023
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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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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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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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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10/26/2021
An extended physics informed neural network for preliminary analysis of parametric optimal control problems
In this work we propose an extension of physics informed supervised lear...
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10/18/2021
A Dimensionality Reduction Approach for Convolutional Neural Networks
The focus of this paper is the application of classical model order redu...
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10/18/2021
A dynamic mode decomposition extension for the forecasting of parametric dynamical systems
Dynamic mode decomposition (DMD) has recently become a popular tool for ...
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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...
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01/11/2021
Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing
In the field of parametric partial differential equations, shape optimiz...
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12/03/2020
Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems
This work describes the implementation of a data-driven approach for the...
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06/12/2020
A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems
In this work, we present an extension of the genetic algorithm (GA) whic...
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04/23/2020
An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques
This contribution describes the implementation of a data–driven shape op...
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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...
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07/30/2019
