Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Nicola Demo"'
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 11, Iss 1, Pp 1-26 (2024)
Abstract The article presents the application of inductive graph machine learning surrogate models for accurate and efficient prediction of 3D flow for industrial geometries, explicitly focusing here on external aerodynamics for a motorsport case. Th
Externí odkaz:
https://doaj.org/article/771a32b17ca34a52b3ee1d44b9950e96
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)
Abstract In this work, we present GAROM, a new approach for reduced order modeling (ROM) based on generative adversarial networks (GANs). GANs attempt to learn to generate data with the same statistics of the underlying distribution of a dataset, usi
Externí odkaz:
https://doaj.org/article/2994c234aa644c5fa879ce4d240e966e
Publikováno v:
Mathematics in Engineering, Vol 6, Iss 1, Pp 28-44 (2024)
In this work we present a novel approach for modeling complex industrial plants, employing directed graphs to the simulation and automatic reconfiguration after failures. The framework offers the possibility to model the failure propagation, estimati
Externí odkaz:
https://doaj.org/article/f2c1a1c6e1a04738ae580ac9bff84b74
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 10, Iss 1, Pp 1-21 (2023)
Abstract In the present work, we introduce a novel approach to enhance the precision of reduced order models by exploiting a multi-fidelity perspective and DeepONets. Reduced models provide a real-time numerical approximation by simplifying the origi
Externí odkaz:
https://doaj.org/article/c02ce668480c4d2d9e1e52a62c37228b
Publikováno v:
Mathematics in Engineering, Vol 4, Iss 3, Pp 1-16 (2022)
This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This app
Externí odkaz:
https://doaj.org/article/071ce1b66a15486e9dae59f7b0b5477a
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 7, Iss 1, Pp 1-19 (2020)
Abstract 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 order method is based on dynamic mode decomposition (DMD) an
Externí odkaz:
https://doaj.org/article/3ec278792f504ba1abd865cfe1992149
Publikováno v:
Journal of Marine Science and Engineering, Vol 9, Iss 2, p 185 (2021)
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-driven framework involving multiple reduction techniques is propo
Externí odkaz:
https://doaj.org/article/840dbdf5a79249619fe34f3980c4317f
Publikováno v:
Computers & Mathematics with Applications. 143:383-396
In this work we propose an extension of physics informed supervised learning strategies to parametric partial differential equations. Indeed, even if the latter are indisputably useful in many applications, they can be computationally expensive most
Publikováno v:
Journal of Scientific Computing. 95
In this work, we propose a model order reduction framework to deal with inverse problems in a non-intrusive setting. Inverse problems, especially in a partial differential equation context, require a huge computational load due to the iterative optim
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks
As a major breakthrough in artificial intelligence and deep learning, Convolutional Neural Networks have achieved an impressive success in solving many problems in several fields including computer vision and image processing. Real-time performance,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08df8bf83686afbbc9a4f937df20d650
http://arxiv.org/abs/2207.13551
http://arxiv.org/abs/2207.13551