Zobrazeno 1 - 10
of 189
pro vyhledávání: '"Calabrò, Francesco"'
Autor:
Auricchio, Ferdinando, Belardo, Maria Roberta, Fabiani, Gianluca, Calabrò, Francesco, Pascaner, Ariel F.
In the present paper, we consider one-hidden layer ANNs with a feedforward architecture, also referred to as shallow or two-layer networks, so that the structure is determined by the number and types of neurons. The determination of the parameters th
Externí odkaz:
http://arxiv.org/abs/2308.10720
Autor:
Calabrò, Francesco, Cuomo, Salvatore, di Serafino, Daniela, Izzo, Giuseppe, Messina, Eleonora
We investigate the resolution of parabolic PDEs via Extreme Learning Machine (ELMs) Neural Networks, which have a single hidden layer and can be trained at a modest computational cost as compared with Deep Learning Neural Networks. Our approach addre
Externí odkaz:
http://arxiv.org/abs/2206.00452
Publikováno v:
In Neurocomputing 28 September 2024 599
Autor:
Galaris, Evangelos, Fabiani, Gianluca, Calabrò, Francesco, di Serafino, Daniela, Siettos, Constantinos
We propose a numerical method based on physics-informed Random Projection Neural Networks for the solution of Initial Value Problems (IVPs) of Ordinary Differential Equations (ODEs) with a focus on stiff problems. We address an Extreme Learning Machi
Externí odkaz:
http://arxiv.org/abs/2108.01584
Publikováno v:
Journal of Scientific Computing 89, 44 (2021)
We address a new numerical scheme based on a class of machine learning methods, the so-called Extreme Learning Machines with both sigmoidal and radial-basis functions, for the computation of steady-state solutions and the construction of (one dimensi
Externí odkaz:
http://arxiv.org/abs/2104.06116
Publikováno v:
In Journal of Computational and Applied Mathematics 15 January 2024 436
Publikováno v:
Computer Methods in Applied Mechanics and Engineering, 387, 2021
We introduce a new numerical method based on machine learning to approximate the solution of elliptic partial differential equations with collocation using a set of sigmoidal functions. We show that a feedforward neural network with a single hidden l
Externí odkaz:
http://arxiv.org/abs/2012.05871