57 citations · 618 across the 33 of their papers we have counts for
4 papers · 1 filter
Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems
Giulio Ortali, Nicola Demo, Gianluigi Rozza
This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogon…
A supervised learning approach involving active subspaces for an efficient genetic algorithm in high-dimensional optimization problems
Nicola Demo, Marco Tezzele, Gianluigi Rozza
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 low…
An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques
Nicola Demo, Giulio Ortali, Gianluca Gustin +2
This contribution describes the implementation of a data--driven shape optimization pipeline in a naval architecture application. We adopt reduced order models (ROMs) in order to i…
Enhancing CFD predictions in shape design problems by model and parameter space reduction
Marco Tezzele, Nicola Demo, Giovanni Stabile +2
In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced…