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Towards a machine learning pipeline in reduced order modelling for
  inverse problems: neural networks for boundary parametrization,
  dimensionality reduction and solution manifold approximation

Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation

Journal of Scientific Computing (J. Sci. Comput.), 2022
26 October 2022
A. Ivagnes
N. Demo
G. Rozza
    MedImAI4CE
ArXiv (abs)PDFHTML

Papers citing "Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation"

2 / 2 papers shown
Title
Generative Adversarial Reduced Order Modelling
Generative Adversarial Reduced Order ModellingScientific Reports (Sci Rep), 2023
Dario Coscia
N. Demo
G. Rozza
GANAI4CE
337
8
0
25 May 2023
A DeepONet multi-fidelity approach for residual learning in reduced
  order modeling
A DeepONet multi-fidelity approach for residual learning in reduced order modelingAdvanced Modeling and Simulation in Engineering Sciences (AMSES), 2023
N. Demo
M. Tezzele
G. Rozza
225
24
0
24 Feb 2023
1