We present ElastoGen, a knowledge-driven model that generates physically
accurate and coherent 4D elastodynamics. Instead of relying on petabyte-scale
data-driven learning, ElastoGen leverages the principles of physics-in-the-loop
and learns from established physical knowledge, such as partial differential
equations and their numerical solutions. The core idea of ElastoGen is
converting the global differential operator, corresponding to the nonlinear
elastodynamic equations, into iterative local convolution-like operations,
which naturally fit modern neural networks. Each network module is specifically
designed to support this goal rather than functioning as a black box. As a
result, ElastoGen is exceptionally lightweight in terms of both training
requirements and network scale. Additionally, due to its alignment with
physical procedures, ElastoGen efficiently generates accurate dynamics for a
wide range of hyperelastic materials and can be easily integrated with upstream
and downstream deep modules to enable end-to-end 4D generation.

通过将全局微分算子转换为迭代的局部卷积操作，ElastoGen 利用物理学原理进行训练，生成准确的 4D 弹性动力学模型，并且与深度学习模块进行无缝整合。