Zobrazeno 1 - 5
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pro vyhledávání: '"Franco, Nicola R."'
We propose a new reduced order modeling strategy for tackling parametrized Partial Differential Equations (PDEs) with linear constraints, in particular Darcy flow systems in which the constraint is given by mass conservation. Our approach employs cla
Externí odkaz:
http://arxiv.org/abs/2311.14554
Publikováno v:
Mathematics of Computation (AMS), 2022
Within the framework of parameter dependent PDEs, we develop a constructive approach based on Deep Neural Networks for the efficient approximation of the parameter-to-solution map. The research is motivated by the limitations and drawbacks of state-o
Externí odkaz:
http://arxiv.org/abs/2103.06183
Autor:
Massi, Michela C., Franco, Nicola R., Ieva, Francesca, Manzoni, Andrea, Paganoni, Anna Maria, Zunino, Paolo
Logistic Regression (LR) is a widely used statistical method in empirical binary classification studies. However, real-life scenarios oftentimes share complexities that prevent from the use of the as-is LR model, and instead highlight the need to inc
Externí odkaz:
http://arxiv.org/abs/2102.12974
Autor:
Massi, Michela C.1,2 (AUTHOR) michela.massi@fht.org, Franco, Nicola R.1 (AUTHOR), Manzoni, Andrea1 (AUTHOR), Paganoni, Anna Maria1 (AUTHOR), Park, Hanla A.3,4 (AUTHOR), Hoffmeister, Michael5 (AUTHOR), Brenner, Hermann5,6,7 (AUTHOR), Chang-Claude, Jenny3,8 (AUTHOR), Ieva, Francesca1,2 (AUTHOR), Zunino, Paolo1 (AUTHOR)
Publikováno v:
PLoS ONE. 2/10/2023, Vol. 16 Issue 2, p1-27. 27p.
Akademický článek
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