8 citations · 24 across the 15 of their papers we have counts for
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cs.LG2022★ 5 cited
Fine-grained differentiable physics: a yarn-level model for fabrics
Deshan Gong, Zhanxing Zhu, Andrew J. Bulpitt +1
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 i…
cs.LG2021★ 4 cited
High-order Differentiable Autoencoder for Nonlinear Model Reduction
Siyuan Shen, Yang Yin, Tianjia Shao +4
This paper provides a new avenue for exploiting deep neural networks to improve physics-based simulation. Specifically, we integrate the classic Lagrangian mechanics with a deep au…