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

T. Kadeethum, D. O'Malley, Y. Choi, H. Viswanathan, N. Bouklas, and H. Yoon. CoRR, (2021)

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Nikolaos Giannakopoulos University of Stuttgart

Nikolaos Papanikolaou University of Stuttgart

Nikolaos Karadimitriou University of Stuttgart

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Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties.T. Kadeethum, D. O'Malley, Y. Choi, H. Viswanathan, N. Bouklas, and H. Yoon. CoRR, (2021)The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity.J. Fuhg, and N. Bouklas. J. Comput. Phys., (2022)Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds.T. Kadeethum, F. Ballarin, D. O'Malley, Y. Choi, N. Bouklas, and H. Yoon. CoRR, (2022)

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Disambiguation of "Bouklas, Nikolaos"

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

Please choose a person to relate this publication to

Nikolaos Giannakopoulos University of Stuttgart

Nikolaos Papanikolaou University of Stuttgart

Nikolaos Karadimitriou University of Stuttgart

Other publications of authors with the same name

Disambiguation

PUMA

Disambiguation of "Bouklas, Nikolaos"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties.

Please choose a person to relate this publication to

Nikolaos Giannakopoulos University of Stuttgart

Nikolaos Papanikolaou University of Stuttgart

Nikolaos Karadimitriou University of Stuttgart

Other publications of authors with the same name

Disambiguation

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