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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

26 October 2022
A. Ivagnes
N. Demo
G. Rozza
    MedIm
    AI4CE
ArXivPDFHTML

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 Modelling
Dario Coscia
N. Demo
G. Rozza
GAN
AI4CE
45
5
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 modeling
N. Demo
M. Tezzele
G. Rozza
30
19
0
24 Feb 2023
1