  Traditional fluid flow predictions require large computational resources.
Despite recent progress in parallel and GPU computing, the ability to run fluid
flow predictions in real-time is often infeasible. Recently developed machine
learning approaches, which are trained on high-fidelity data, perform
unsatisfactorily outside the training set and remove the ability of utilising
legacy codes after training. We propose a novel methodology that uses a deep
learning approach that can be used within a low-fidelity fluid flow solver to
significantly increase the accuracy of the low-fidelity simulations. The
resulting solver enables accurate while reducing computational times up to 100
times. The deep neural network is trained on a combination of low- and
high-fidelity data, and the resulting solver is referred to as a multi-fidelity
solver. The proposed methodology is demonstrated by means of enhancing a fluid
flow simulator, known as PIC/FLIP, which is a popular fluid flow simulator in
the field of computer generated imagery.
