Latent-space Dynamics for Reduced Deformable Simulation
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
We propose the first reduced model simulation framework for deformable solid dynamics using autoencoder neural networks. We provide a data-driven approach to generating nonlinear reduced spaces for deformation dynamics. In contrast to previous methods using machine learning which accelerate simulation by approximating the time-stepping function, we solve the true equations of motion in the latent-space using a variational formulation of implicit integration. Our approach produces drastically smaller reduced spaces than conventional linear model reduction, improving performance and robustness. Furthermore, our method works well with existing force-approximation cubature methods.
Description
@article{10.1111:cgf.13645,
journal = {Computer Graphics Forum},
title = {{Latent-space Dynamics for Reduced Deformable Simulation}},
author = {Fulton, Lawson and Modi, Vismay and Duvenaud, David and Levin, David I. W. and Jacobson, Alec},
year = {2019},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13645}
}