91 research outputs found
Accelerating Eulerian Fluid Simulation With Convolutional Networks
Efficient simulation of the Navier-Stokes equations for fluid flow is a long
standing problem in applied mathematics, for which state-of-the-art methods
require large compute resources. In this work, we propose a data-driven
approach that leverages the approximation power of deep-learning with the
precision of standard solvers to obtain fast and highly realistic simulations.
Our method solves the incompressible Euler equations using the standard
operator splitting method, in which a large sparse linear system with many free
parameters must be solved. We use a Convolutional Network with a highly
tailored architecture, trained using a novel unsupervised learning framework to
solve the linear system. We present real-time 2D and 3D simulations that
outperform recently proposed data-driven methods; the obtained results are
realistic and show good generalization properties.Comment: Significant revisio
RealitySketch: Embedding Responsive Graphics and Visualizations in AR through Dynamic Sketching
We present RealitySketch, an augmented reality interface for sketching
interactive graphics and visualizations. In recent years, an increasing number
of AR sketching tools enable users to draw and embed sketches in the real
world. However, with the current tools, sketched contents are inherently
static, floating in mid air without responding to the real world. This paper
introduces a new way to embed dynamic and responsive graphics in the real
world. In RealitySketch, the user draws graphical elements on a mobile AR
screen and binds them with physical objects in real-time and improvisational
ways, so that the sketched elements dynamically move with the corresponding
physical motion. The user can also quickly visualize and analyze real-world
phenomena through responsive graph plots or interactive visualizations. This
paper contributes to a set of interaction techniques that enable capturing,
parameterizing, and visualizing real-world motion without pre-defined programs
and configurations. Finally, we demonstrate our tool with several application
scenarios, including physics education, sports training, and in-situ tangible
interfaces.Comment: UIST 202
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