Time-Dependent Machine Learning for Volumetric Simulation

Abstract

We explore the application of a time-dependent machine learning framework to art direction of volumetric simulations. We show the benefit of the time dependency inherent to the ODE-net model when used in conjunction with simulation sequences. Unlike other machine learning methods which maintain a uniform timestep constraint during evaluation, the ODE-net framework is able to generate results for arbitrary time samples. We demonstrate how this non-uniform time step evaluation can be leveraged for use in artistic direction tasks. We specifically apply the model to the retiming of volumetric simulations to showcase the ability of the machine learning method to properly predict arbitrary time steps. We show that with minimal training data, the model is able to generalize over several simulation sequences with similar parameters.