"Highly-scalable, physics-informed GANs for learning solutions of ..."

Liu Yang et al. (2019)

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DOI: —

access: open

type: Informal or Other Publication

metadata version: 2019-10-31

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  - electronic edition @ arxiv.org (open access)
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  dblp key:
  - journals/corr/abs-1910-13444
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  persistent URL:
  - https://dblp.org/rec/journals/corr/abs-1910-13444
Liu Yang, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston, Prabhat, George E. Karniadakis:
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. CoRR abs/1910.13444 (2019)

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