# 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…

## 6 Citations

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An infused approach for MRE reconstruction by integrating the statistical representation of the physical laws of harmonic motions and learning-based prior is proposed and results of elasticity reconstruction verify the effectiveness of the proposed approach.

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