  We developed a general deep learning framework, FluidGAN, 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 helps understand deterministic
multiphysics phenomena where the underlying physical model is complex or
unknown.
