Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
Name
3610548.3618207.pdf
Size
21.99 MB
Format
Adobe PDF
Checksum (MD5)
adc0f5aa962389d82e246e9601cc48e6
Author(s)
Zong, Zeshun
Li, Xuan
Li, Minchen
Chiaramonte, Maurizio M.
Matusik, Wojciech
Grinspun, Eitan
Carlberg, Kevin
Jiang, Chenfanfu
Chen, Peter Yichen
Date Issued
December 10, 2023
Publisher
ACM|SIGGRAPH Asia 2023 Conference Papers
Citation
Zong, Zeshun, Li, Xuan, Li, Minchen, Chiaramonte, Maurizio M., Matusik, Wojciech et al. 2023. "Neural Stress Fields for Reduced-order Elastoplasticity and Fracture."
Version
Final published version
Abstract
We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models like the Material Point Method (MPM) faithfully simulate large-deformation elastoplasticity and fracture mechanics. However, their long runtime and large memory consumption render them unsuitable for applications constrained by computation time and memory usage, e.g., virtual reality. To overcome these barriers, we propose a reduced-order framework. Our key innovation is training a low-dimensional manifold for the Kirchhoff stress field via an implicit neural representation. This low-dimensional neural stress field (NSF) enables efficient evaluations of stress values and, correspondingly, internal forces at arbitrary spatial locations. In addition, we also train neural deformation and affine fields to build low-dimensional manifolds for the deformation and affine momentum fields. These neural stress, deformation, and affine fields share the same low-dimensional latent space, which uniquely embeds the high-dimensional simulation state. After training, we run new simulations by evolving in this single latent space, which drastically reduces the computation time and memory consumption. Our general continuum-mechanics-based reduced-order framework is applicable to any phenomena governed by the elastodynamics equation. To showcase the versatility of our framework, we simulate a wide range of material behaviors, including elastica, sand, metal, non-Newtonian fluids, fracture, contact, and collision. We demonstrate dimension reduction by up to 100,000 × and time savings by up to 10 ×.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3610548.3618207
