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Latent‐space Dynamics for Reduced Deformable Simulation
Author(s) -
Fulton Lawson,
Modi Vismay,
Duvenaud David,
Levin David I. W.,
Jacobson Alec
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13645
Subject(s) - robustness (evolution) , computer science , nonlinear system , autoencoder , function space , dynamics (music) , space (punctuation) , algorithm , artificial neural network , mathematics , artificial intelligence , mathematical analysis , biochemistry , chemistry , physics , quantum mechanics , acoustics , gene , operating system
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.