Fine-grained differentiable physics: a yarn-level model for fabrics. (arXiv:2202.00504v1 [cs.LG])

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 physical phenomena. Fine-grained
models are still to be developed to incorporate sophisticated …

arxiv fabrics physics