119 research outputs found
Deep Learning Convective Flow Using Conditional Generative Adversarial Networks
We developed a general deep learning framework, FluidGAN, that is capable of
learning and predicting time-dependent convective flow coupled with energy
transport. FluidGAN is thoroughly data-driven with high speed and accuracy and
satisfies the physics of fluid without any prior knowledge of underlying fluid
and energy transport physics. FluidGAN also learns the coupling between
velocity, pressure and temperature fields. Our framework could be used to learn
deterministic multiphysics phenomena where the underlying physical model is
complex or unknown
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