Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties

Machine learning-based data-driven modeling can allow computationally efficient time-dependent solutions of PDEs, such as those that describe subsurface multiphysical problems. In this work, our previous approach (Kadeethum et al., 2021d) of conditional generative adversarial networks (cGAN) develop...

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Veröffentlicht in:	Computers & geosciences 2022-10, Vol.167, p.105212, Article 105212
Hauptverfasser:	Kadeethum, T., O’Malley, D., Choi, Y., Viswanathan, H.S., Bouklas, N., Yoon, H.
Format:	Artikel
Sprache:	eng
Schlagworte:	Data-driven Finite element Generative adversarial networks MATHEMATICS AND COMPUTING Non-intrusive Poroelasticity Reduced order modeling
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