  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.
