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 …

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 …

We present a data-driven technique to instantly predict how fluid flows around various three-
dimensional objects. Such simulation is useful for computational fabrication and …

We propose the first reduced model simulation framework for deformable solid dynamics
using autoencoder neural networks. We provide a data‐driven approach to generating …

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

We present a new subspace integration method that is capable of efficiently adding and
subtracting dynamics from an existing high-resolution fluid simulation. We show how to …

We present a novel method for elastic animation editing with space-time constraints. In a
sharp departure from previous approaches, we not only optimize control forces added to a …

We present an efficient new subspace method for simulating the self-contact of articulated
deformable bodies, such as characters. Self-contact is highly structured in this setting, as the …

The Laplacian Eigenfunction method for fluid simulation, which we refer to as Eigenfluids,
introduced an elegant new way to capture intricate fluid flows with near-zero viscosity …

This work presents a method for efficiently simplifying the pressure projection step in a liquid
simulation. We first devise a straightforward dimension reduction technique that dramatically …