• Corpus ID: 219559293

Inverse Estimation of Elastic Modulus Using Physics-Informed Generative Adversarial Networks

@article{Warner2020InverseEO,
  title={Inverse Estimation of Elastic Modulus Using Physics-Informed Generative Adversarial Networks},
  author={James E. Warner and Julian Cuevas and Geoffrey F. Bomarito and Patrick E. Leser and William P. Leser},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.05791}
}
While standard generative adversarial networks (GANs) rely solely on training data to learn unknown probability distributions, physics-informed GANs (PI-GANs) encode physical laws in the form of stochastic partial differential equations (PDEs) using auto differentiation. By relating observed data to unobserved quantities of interest through PDEs, PI-GANs allow for the estimation of underlying probability distributions without their direct measurement (i.e. inverse problems). The scalable nature… 

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