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We propose a novel differentiable physics engine for system identification ofcomplex spring-rod assemblies. Unlike black-box data-driven methods forlearning the evolution of a dynamical system and its parameters, we modularizethe design of our engine using a discrete form of the governing equations ofmotion, similar to a traditional physics engine. We further reduce thedimension from 3D to 1D for each module, which allows efficient learning ofsystem parameters using linear regression. As a side benefit, the regressionparameters correspond to physical quantities, such as spring stiffness or themass of the rod, making the pipeline explainable. The approach significantlyreduces the amount of training data required, and also avoids iterativeidentification of data sampling and model training. We compare the performanceof the proposed engine with previous solutions, and demonstrate its efficacy ontensegrity systems, such as NASA's icosahedron.

A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines