Our two papers on learning temporal predictions and reduced representations for fluids have been accepted to the CGF Journal and will be presented at Eurographics 2019 in Milano! Congratulations to Steffen, Moritz, Byungsoo and Vinicius!
Our work explores methods for the data-driven inference of temporal evolutions of physical functions with deep learning techniques. More specifically, we target fluid flow problems, and we propose a novel network architecture to predict the changes of the pressure field over time. The central challenge in this context is the high dimensionality of Eulerian space-time data sets. Key for arriving at a feasible algorithm is a technique for dimensionality reduction based on convolutional neural networks, as well as a special architecture for temporal prediction. We demonstrate that dense 3D+time functions of physics system can be predicted with neural networks, and we arrive at a neural-network based simulation algorithm with practical speed-ups. We demonstrate the capabilities of our method with a series of complex liquid simulations, and with a set of single-phase simulations. Our method predicts pressure fields very efficiently. It is more than two orders of magnitudes faster than a regular solver. Additionally, we present and discuss a series of detailed evaluations for the different components of our algorithm.
This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to the capability of deep learning architectures to learn representative features of the data, our generative model is able to accurately approximate the training data set, while providing plausible interpolated in-betweens. The proposed generative model is optimized for fluids by a novel loss function that guarantees divergence-free velocity fields at all times. In addition, we demonstrate that we can handle complex parameterizations in reduced spaces, and advance simulations in time by integrating in the latent space with a second network. Our method models a wide variety of fluid behaviors, thus enabling applications such as fast construction of simulations, interpolation of fluids with different parameters, time re-sampling, latent space simulations, and compression of fluid simulation data. Reconstructed velocity fields are generated up to 700x faster than traditional CPU solvers, while achieving compression rates of over 1300x.
