research
∙
08/02/2023
A digital twin framework for civil engineering structures
The digital twin concept represents an appealing opportunity to advance ...
share
research
∙
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...
research
∙
06/02/2022
A multi-fidelity approach coupling parameter space reduction and non-intrusive POD with application to structural optimization of passenger ship hulls
Nowadays, the shipbuilding industry is facing a radical change towards s...
research
∙
11/27/2021
A data-driven partitioned approach for the resolution of time-dependent optimal control problems with dynamic mode decomposition
This work recasts time-dependent optimal control problems governed by pa...
research
∙
10/27/2021
Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering
Multi-fidelity models are of great importance due to their capability of...
research
∙
07/22/2021
A local approach to parameter space reduction for regression and classification tasks
Frequently, the parameter space, chosen for shape design or other applic...
research
∙
05/14/2021
ATHENA: Advanced Techniques for High Dimensional Parameter Spaces to Enhance Numerical Analysis
ATHENA is an open source Python package for reduction in parameter space...
research
∙
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...
research
∙
10/16/2020
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces
Gaussian processes are employed for non-parametric regression in a Bayes...
research
∙
08/27/2020
Kernel-based Active Subspaces with application to CFD parametric problems using Discontinuous Galerkin method
A new method to perform a nonlinear reduction in parameter spaces is pro...
research
∙
06/25/2020
On the comparison of LES data-driven reduced order approaches for hydroacoustic analysis
In this work, Dynamic Mode Decomposition (DMD) and Proper Orthogonal Dec...
research
∙
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...
research
∙
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...
research
∙
11/20/2019
Basic Ideas and Tools for Projection-Based Model Reduction of Parametric Partial Differential Equations
We provide first the functional analysis background required for reduced...
research
∙
07/30/2019
A non-intrusive approach for proper orthogonal decomposition modal coefficients reconstruction through active subspaces
Reduced order modeling (ROM) provides an efficient framework to compute ...
research
∙
05/15/2019