  Computational Fluid Dynamics (CFD) is a hugely important subject with
applications in almost every engineering field, however, fluid simulations are
extremely computationally and memory demanding. Towards this end, we present
Lat-Net, a method for compressing both the computation time and memory usage of
Lattice Boltzmann flow simulations using deep neural networks. Lat-Net employs
convolutional autoencoders and residual connections in a fully differentiable
scheme to compress the state size of a simulation and learn the dynamics on
this compressed form. The result is a computationally and memory efficient
neural network that can be iterated and queried to reproduce a fluid
simulation. We show that once Lat-Net is trained, it can generalize to large
grid sizes and complex geometries while maintaining accuracy. We also show that
Lat-Net is a general method for compressing other Lattice Boltzmann based
simulations such as Electromagnetism.
