• Corpus ID: 246441923

Fine-grained differentiable physics: a yarn-level model for fabrics

@article{Gong2022FinegrainedDP,
  title={Fine-grained differentiable physics: a yarn-level model for fabrics},
  author={Deshan Gong and Zhanxing Zhu and Andrew J. Bulpitt and He Wang},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.00504}
}
Differentiable physics modeling combines physics models with gradient-based learning to provide model explicability and data efficiency. It has been used to learn dynamics, solve inverse problems and facilitate design, and is at its inception of impact. Current successes have concentrated on general physics models such as rigid bodies, deformable sheets, etc, assuming relatively simple structures and forces. Their granularity is intrinsically coarse and therefore incapable of modelling complex… 

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