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 re-simulating the data with the underlying CPU
solver, while achieving compression rates of up to 1300x.

本研究介绍了一种能够从降维参数集合综合流体模拟的新型生成模型，采用卷积神经网络进行训练，通过学习数据的代表性特征，能够对训练数据集进行精确近似而同时提供可信的插值。该模型经过优化，实现了全时刻无发散，能够处理复杂参数化并通过潜在空间积分进一步模拟。其模型包括多种流体行为，可用于模拟加速、流体插值、时间重采样、潜在空间模拟及流体模拟数据压缩，重建速度场比外部 CPU 求解器重复模拟节省 700 倍时间，压缩率达到 1300 倍以上。