Latent-space Dynamics for Reduced Deformable Simulation

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Date
2019
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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.
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@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
} }
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