Recurrent neural networks are widely used on time series data, yet such models often
ignore the underlying physical structures in such sequences. A new class of physics-based …

Physics-based fluid simulation has played an increasingly important role in the computer
graphics community. Recent methods in this area have greatly improved the generation of …

Direction fields and vector fields play an increasingly important role in computer graphics
and geometry processing. The synthesis of directional fields on surfaces, or other spatial …

Notions of similarity and correspondence between geometric shapes and images are central
to many tasks in geometry processing, computer vision, and computer graphics. The goal of …

The animation of delicate vortical structures of gas and liquids has been of great interest in
computer graphics. However, common velocity-based fluid solvers can damp the vortical …

This study presents the formulation and validation of a three-dimensional Flow Lattice Model
(FLM) with application to the Hygro-Thermo-Chemical (HTC) model for analysis of moisture …

Disentangling complex data to its latent factors of variation is a fundamental task in
representation learning. Existing work on sequential disentanglement mostly provides two …

We present a model-reduced variational Eulerian integrator for incompressible fluids, which
combines the efficiency gains of dimension reduction, the qualitative robustness of coarse …

Generating realistic time series data is important for many engineering and scientific
applications. Existing work tackles this problem using generative adversarial networks …

Previous research on animations of soap bubbles, films, and foams largely focuses on the
motion and geometric shape of the bubble surface. These works neglect the evolution of the …