Search Results for author:
Found 18 papers, 13 papers with code
A digital twin framework for civil engineering structures
This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures.
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 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.
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
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
