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of 154
pro vyhledávání: '"DA RONCH, Andrea"'
Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads
Multi-fidelity models are becoming more prevalent in engineering, particularly in aerospace, as they combine both the computational efficiency of low-fidelity models with the high accuracy of higher-fidelity simulations. Various state-of-the-art tech
Externí odkaz:
http://arxiv.org/abs/2407.05684
This paper focuses on addressing challenges posed by non-homogeneous unstructured grids, commonly used in Computational Fluid Dynamics (CFD). Their prevalence in CFD scenarios has motivated the exploration of innovative approaches for generating redu
Externí odkaz:
http://arxiv.org/abs/2405.04396
Autor:
WANG, Zhigang, SUN, Xiasheng, YANG, Yu, GE, Wenjie, LI, Daochun, XIANG, Jinwu, BAO, Panpan, WU, Qi, DA RONCH, Andrea
Publikováno v:
In Chinese Journal of Aeronautics July 2024 37(7):285-300
A methodology to generate sparse Galerkin models of chaotic/unsteady fluid flows containing a minimal number of active triadic interactions is proposed. The key idea is to find an appropriate set of basis functions for the projection representing ele
Externí odkaz:
http://arxiv.org/abs/2105.06753
In this paper, sparsity-promoting regression techniques are employed to automatically identify from data relevant triadic interactions between modal structures in large Galerkin-based models of two-dimensional unsteady flows. The approach produces in
Externí odkaz:
http://arxiv.org/abs/2006.05736
Publikováno v:
In Composite Structures 1 October 2023 321
Publikováno v:
In Aerospace Science and Technology July 2023 138
Publikováno v:
In Aerospace Science and Technology January 2023 132
Akademický článek
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Publikováno v:
Aircraft Engineering and Aerospace Technology, 2022, Vol. 94, Issue 6, pp. 881-894.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/AEAT-08-2021-0227