期刊
COMPUTER GRAPHICS FORUM
卷 38, 期 2, 页码 379-391出版社
WILEY
DOI: 10.1111/cgf.13645
关键词
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资金
- NSERC [RGPIN-2017-05235, RGPIN-2017-05524, RGPAS-2017-507938, RGPAS-2017-507909]
- Connaught Funds [NR2016-17]
- Canada Research Chairs Program
- Fields Institute
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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