Zobrazeno 1 - 10
of 247
pro vyhledávání: '"Gianluigi Rozza"'
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:
AIP Advances, Vol 13, Iss 5, Pp 055024-055024-16 (2023)
Within OpenFOAM, we develop a pressure-based solver for the Euler equations written in conservative form using density, momentum, and total energy as variables. Under simplifying assumptions, these equations are used to describe non-hydrostatic atmos
Externí odkaz:
https://doaj.org/article/20f70bd3c2344e3fa73db90b7b01efe1
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:
Mathematical and Computational Applications, Vol 28, Iss 1, p 16 (2023)
This Special Issue comprises the first collection of papers submitted by the Editorial Board Members (EBMs) of the journal Mathematical and Computational Applications (MCA), as well as outstanding scholars working in the core research fields of MCA [
Externí odkaz:
https://doaj.org/article/004c9839190d42469583238b0326a266
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 5, Iss 1, Pp 1-19 (2018)
Abstract 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 considered is the one of the simulation of the hydrodyn
Externí odkaz:
https://doaj.org/article/5cc3f2e17ad24be89c304cd5b6569022
Publikováno v:
Fluids, Vol 6, Iss 8, p 296 (2021)
Geometrically parametrized partial differential equations are currently widely used in many different fields, such as shape optimization processes or patient-specific surgery studies. The focus of this work is some advances on this topic, capable of
Externí odkaz:
https://doaj.org/article/2abb9c7f4fe941b6b2e745cec103f14e