Reduced basis methods for time-dependent problems
Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …
study of real-world phenomena in applied science and engineering. Computational methods …
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 fusing information
coming from different numerical simulations, surrogates, and sensors. We focus on the …
coming from different numerical simulations, surrogates, and sensors. We focus on the …
[图书][B] Advanced reduced order methods and applications in computational fluid dynamics
Reduced order modeling is an important and fast-growing research field in computational
science and engineering, motivated by several reasons, of which we mention just a few …
science and engineering, motivated by several reasons, of which we mention just a few …
[HTML][HTML] Hull shape design optimization with parameter space and model reductions, and self-learning mesh morphing
In the field of parametric partial differential equations, shape optimization represents a
challenging problem due to the required computational resources. In this contribution, a data …
challenging problem due to the required computational resources. In this contribution, a data …
A non-intrusive approach for the reconstruction of POD modal coefficients through active subspaces
Reduced order modeling (ROM) provides an efficient framework to compute solutions of
parametric problems. Basically, it exploits a set of precomputed high-fidelity solutions …
parametric problems. Basically, it exploits a set of precomputed high-fidelity solutions …
[HTML][HTML] PyGeM: Python geometrical morphing
PyGeM is an open source Python package which allows to easily parametrize and deform
3D object described by CAD files or 3D meshes. It implements several morphing techniques …
3D object described by CAD files or 3D meshes. It implements several morphing techniques …
A dynamic mode decomposition extension for the forecasting of parametric dynamical systems
Dynamic mode decomposition (DMD) has recently become a popular tool for the
nonintrusive analysis of dynamical systems. Exploiting proper orthogonal decomposition …
nonintrusive analysis of dynamical systems. Exploiting proper orthogonal decomposition …
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. The non-intrusive reduced …
prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced …
A multifidelity approach coupling parameter space reduction and nonintrusive POD with application to structural optimization of passenger ship hulls
Nowadays, the shipbuilding industry is facing a radical change toward solutions with a
smaller environmental impact. This can be achieved with low emissions engines, optimized …
smaller environmental impact. This can be achieved with low emissions engines, optimized …
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 …
reduction techniques for industrial and applied mathematics, as emerging technology for …