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.

翻译：本文提出ElastoGen，一种知识驱动的模型，能够生成物理精确且连贯的四维弹性动力学。与依赖PB级数据驱动学习的方法不同，ElastoGen利用物理在环原理，从已建立的物理知识（如偏微分方程及其数值解）中学习。ElastoGen的核心思想是将对应于非线性弹性动力学方程的全局微分算子转换为迭代的局部类卷积操作，这自然契合现代神经网络架构。每个网络模块都专门设计以支持这一目标，而非作为黑箱运行。因此，ElastoGen在训练需求和网络规模方面都极为轻量。此外，由于其与物理过程的对齐性，ElastoGen能高效生成多种超弹性材料的精确动力学行为，并可轻松与上下游深度模块集成，实现端到端的四维生成。