# Deep Learning of Dynamic Subsurface Flow via Theory-guided Generative Adversarial Network

@article{He2020DeepLO, title={Deep Learning of Dynamic Subsurface Flow via Theory-guided Generative Adversarial Network}, author={Tianhao He and Dongxiao Zhang}, journal={ArXiv}, year={2020}, volume={abs/2006.13305} }

Generative adversarial network (GAN) has been shown to be useful in various applications, such as image recognition, text processing and scientific computing, due its strong ability to learn complex data distributions. In this study, a theory-guided generative adversarial network (TgGAN) is proposed to solve dynamic partial differential equations (PDEs). Different from standard GANs, the training term is no longer the true data and the generated data, but rather their residuals. In additionβ¦Β Expand

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