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In this paper, we propose several efficient architectures of neural networks, which can be used to exploit this idea. The purpose of our research was to specifically target a liquid simulation problem. The central challenge for us was to create an efficient solution capable of approximating Position Based Fluid [Macklin and M\u00fcller 2013] solver. It requires the network to produce an accurate output at particles located in a continuous space and be significantly faster than the GPU based simulation. We achieved this by using modern sub-pixel convolution techniques originally used for image super-resolution. 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