  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 material
structures and force interactions with gradient-based learning. Following this
motivation, we propose a new differentiable fabrics model for composite
materials such as cloths, where we dive into the granularity of yarns and model
individual yarn physics and yarn-to-yarn interactions. To this end, we propose
several differentiable forces, whose counterparts in empirical physics are
indifferentiable, to facilitate gradient-based learning. These forces, albeit
applied to cloths, are ubiquitous in various physical systems. Through
comprehensive evaluation and comparison, we demonstrate our model's
explicability in learning meaningful physical parameters, versatility in
incorporating complex physical structures and heterogeneous materials,
data-efficiency in learning, and high-fidelity in capturing subtle dynamics.
