In this article, a physics aware deep learning model is introduced for
multiphase flow problems. The deep learning model is shown to be capable of
capturing complex physics phenomena such as saturation front, which is even
challenging for numerical solvers due to the instability. We display the
preciseness of the solution domain delivered by deep learning models and the
low cost of deploying this model for complex physics problems, showing the
versatile character of this method and bringing it to new areas. This will
require more allocation points and more careful design of the deep learning
model architectures and residual neural network can be a potential candidate.
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