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 …
Free-form deformation, mesh morphing and reduced-order methods: enablers for efficient aerodynamic shape optimisation
F Salmoiraghi, A Scardigli, H Telib… - International Journal of …, 2018 - Taylor & Francis
In this work, we provide an integrated pipeline for the model-order reduction of turbulent
flows around parametrised geometries in aerodynamics. In particular, free-form deformation …
flows around parametrised geometries in aerodynamics. In particular, free-form deformation …
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 parameter space reduction. The physical problem …
based on active subspaces properties for parameter space reduction. The physical problem …
[HTML][HTML] Comparison of neural closure models for discretised PDEs
H Melchers, D Crommelin, B Koren… - … & Mathematics with …, 2023 - Elsevier
Neural closure models have recently been proposed as a method for efficiently
approximating small scales in multiscale systems with neural networks. The choice of loss …
approximating small scales in multiscale systems with neural networks. The choice of loss …
[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 …
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 …
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 …