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Found 20 papers, 8 papers with code
Generative Adversarial Reduced Order Modelling
In this work, we present GAROM, a new approach for reduced order modelling (ROM) based on generative adversarial networks (GANs).
A DeepONet multi-fidelity approach for residual learning in reduced order modeling
We propose to couple the model reduction to a machine learning residual learning, such that the above-mentioned error can be learned by a neural network and inferred for new predictions.
A Graph-based Framework for Complex System Simulating and Diagnosis with Automatic Reconfiguration
To model the typical operation of industrial plants, we propose several additions with respect to the standard graphs: 1. a quantitative measure to control the overall residual capacity, 2. nodes of different categories - and then different behaviors - and 3. a fault propagation procedure based on the predecessors and the redundancy of the system.
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
In this work, we propose a model order reduction framework to deal with inverse problems in a non-intrusive setting.
A Continuous Convolutional Trainable Filter for Modelling Unstructured Data
The fundamental building block of a CNN is a trainable filter, represented as a discrete grid, used to perform convolution on discrete input data.
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
As a major breakthrough in artificial intelligence and deep learning, Convolutional Neural Networks have achieved an impressive success in solving many problems in several fields including computer vision and image processing.
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 learning strategies to parametric partial differential equations.
A Dimensionality Reduction Approach for Convolutional Neural Networks
The focus of this paper is the application of classical model order reduction techniques, such as Active Subspaces and Proper Orthogonal Decomposition, to Deep Neural Networks.
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 projection-based reduced order modelling.
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 reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR).
GPR
Numerical Analysis
Numerical Analysis
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) which exploits the supervised learning technique called active subspaces (AS) to evolve the individuals on a lower dimensional space.
Numerical Analysis Numerical Analysis Optimization and Control
Enhancing CFD predictions in shape design problems by model and parameter space reduction
In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile.
Numerical Analysis Numerical Analysis
A non-intrusive approach for proper orthogonal decomposition modal coefficients reconstruction through active subspaces
Using this space, an approximation of the numerical solution for new parameters can be computed in real-time response scenario, thanks to the reduced dimensionality of the problem.
Numerical Analysis Numerical Analysis
A complete data-driven framework for the efficient solution of parametric shape design and optimisation in naval engineering problems
Mandatory ingredient for the ROM methods is the relation between the high-fidelity solutions and the parameters.
Numerical Analysis
Shape optimization through proper orthogonal decomposition with interpolation and dynamic mode decomposition enhanced by active subspaces
In previous works we studied the reduction of the parameter space in naval engineering through AS [38, 10] focusing on different parts of the hull.
Numerical Analysis
Reduced Order Isogeometric Analysis Approach for PDEs in Parametrized Domains
In this contribution, we coupled the isogeometric analysis to a reduced order modelling technique in order to provide a computationally efficient solution in parametric domains.
Numerical Analysis
An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics
In this work we present an integrated computational pipeline involving several model order reduction techniques for industrial and applied mathematics, as emerging technology for product and/or process design procedures.
Numerical Analysis
Model Order Reduction by means of Active Subspaces and Dynamic Mode Decomposition for Parametric Hull Shape Design Hydrodynamics
We present the results of the application of a parameter space reduction methodology based on active subspaces (AS) to the hull hydrodynamic design problem.
Numerical Analysis
Shape Optimization by means of Proper Orthogonal Decomposition and Dynamic Mode Decomposition
Shape optimization is a challenging task in many engineering fields, since the numerical solutions of parametric system may be computationally expensive.
Numerical Analysis
An efficient shape parametrisation by free-form deformation enhanced by active subspace for hull hydrodynamic ship design problems in open source environment
To this end, a fully automated procedure has been implemented to produce several small shape perturbations of an original hull CAD geometry which are then used to carry out high-fidelity flow simulations and collect data for the active subspaces analysis.
Numerical Analysis
