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Virtual twins of nonlinear vibrating multiphysics microstructures:
  physics-based versus deep learning-based approaches

Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches

12 May 2022
G. Gobat
S. Fresca
Andrea Manzoni
A. Frangi
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches"

4 / 4 papers shown
Title
Real-time simulation of parameter-dependent fluid flows through deep
  learning-based reduced order models
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models
S. Fresca
Andrea Manzoni
AI4CE
47
36
0
10 Jun 2021
POD-DL-ROM: enhancing deep learning-based reduced order models for
  nonlinear parametrized PDEs by proper orthogonal decomposition
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition
S. Fresca
Andrea Manzoni
AI4CE
63
215
0
28 Jan 2021
A fast and accurate physics-informed neural network reduced order model
  with shallow masked autoencoder
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
Youngkyu Kim
Youngsoo Choi
David Widemann
T. Zohdi
AI4CE
54
196
0
25 Sep 2020
A comprehensive deep learning-based approach to reduced order modeling
  of nonlinear time-dependent parametrized PDEs
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs
S. Fresca
Luca Dede'
Andrea Manzoni
AI4CE
68
264
0
12 Jan 2020
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