We propose a novel differentiable physics engine for system identification of complex spring-rod assemblies. Unlike black-box data-driven methods for learning the evolution of a dynamical system \emph{and} its parameters, we modularize the design of our engine using a discrete form of the governing equations of motion, similar to a traditional physics engine. We further reduce the dimension from 3D to 1D for each module, which allows efficient learning of system parameters using linear regression. The regression parameters correspond to physical quantities, such as spring stiffness or the mass of the rod, making the pipeline explainable. The approach significantly reduces the amount of training data required, and also avoids iterative identification of data sampling and model training. We compare the performance of the proposed engine with previous solutions, and demonstrate its efficacy on tensegrity systems, such as NASA's icosahedron.

中文翻译：

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通过可微物理引擎识别弹簧杆系统

我们提出了一种新颖的可微物理引擎，用于复杂弹簧杆组件的系统识别。与用于学习动态系统\emph{和}其参数的演化的黑盒数据驱动方法不同，我们使用类似于传统物理引擎的离散形式的运动控制方程来模块化我们的引擎设计。我们进一步将每个模块的维度从 3D 减少到 1D，这样可以使用线性回归有效地学习系统参数。回归参数对应于物理量，例如弹簧刚度或杆的质量，使管道可解释。该方法显着减少了所需的训练数据量，也避免了数据采样和模型训练的迭代识别。