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Found 37 papers, 14 papers with code
A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a Concave Surface
This paper aims to comprehensively investigate the efficacy of various Model Order Reduction (MOR) and deep learning techniques in predicting heat transfer in a pulsed jet impinging on a concave surface.
Deep Reinforcement Learning for the Heat Transfer Control of Pulsating Impinging Jets
Soft Double and Duel DQN achieved better thermal control performance among all the variants due to their efficient learning and action prioritization capabilities.
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
To overcome this limitation, we propose a novel ROM framework that integrates optimal transport (OT) theory and neural network-based methods.
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.
A two stages Deep Learning Architecture for Model Reduction of Parametric Time-Dependent Problems
Parametric time-dependent systems are of a crucial importance in modeling real phenomena, often characterized by non-linear behaviors too.
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.
Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method
Non-affine parametric dependencies, nonlinearities and advection-dominated regimes of the model of interest can result in a slow Kolmogorov n-width decay, which precludes the realization of efficient reduced-order models based on linear subspace approximations.
Towards a Numerical Proof of Turbulence Closure
The development of turbulence closure models, parametrizing the influence of small non-resolved scales on the dynamics of large resolved ones, is an outstanding theoretical challenge with vast applicative relevance.
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 encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself.
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.
An artificial neural network approach to bifurcating phenomena in computational fluid dynamics
This work deals with the investigation of bifurcating fluid phenomena using a reduced order modelling setting aided by artificial 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.
A local approach to parameter space reduction for regression and classification tasks
In this work we propose a new method called local active subspaces (LAS), which explores the synergies of active subspaces with supervised clustering techniques in order to carry out a more efficient dimension reduction in the parameter space.
Hybrid neural network reduced order modelling for turbulent flows with geometric parameters
Geometrically parametrized Partial Differential Equations are nowadays widely used in many different fields as, for example, shape optimization processes or patient specific surgery studies.
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 real-time the mold-steel heat flux in continuous casting mold.
Numerical Analysis Numerical Analysis
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
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces
We can augment the inputs with the observations of low-fidelity models in order to learn a more expressive latent manifold and thus increment the model's accuracy.
Kernel-based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method
Nonlinear extensions to the active subspaces method have brought remarkable results for dimension reduction in the parameter space and response surface design.
Numerical Analysis Numerical Analysis 15A18, 15A60, 41A30, 41A63, 65D15, 65N30
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
Efficient Reduction in Shape Parameter Space Dimension for Ship Propeller Blade Design
AS analysis has also been used to carry out a constrained optimization exploiting response surface method in the reduced parameter space, and a sensitivity analysis based on such surrogate model.
Computational Engineering, Finance, and Science 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
Projection-based reduced order models for a cut finite element method in parametrized domains
This work presents a reduced order modelling technique built on a high fidelity embedded mesh finite element method.
Numerical Analysis Numerical Analysis 78M34, 97N40, 35Q35
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
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
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
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
Combined parameter and model reduction of cardiovascular problems by means of active subspaces and POD-Galerkin methods
In this chapter we introduce a combined parameter and model reduction methodology and present its application to the efficient numerical estimation of a pressure drop in a set of deformed carotids.
Numerical Analysis
Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems
We present the results of the first application in the naval architecture field of a methodology based on active subspaces properties for parameters space reduction.
Numerical Analysis
