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

arXiv: https://arxiv.org/abs/2004.13859 · HTML: https://mlsanity.com/paper/2004.13859/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [gradSim: Differentiable simulation for system identification and visuomotor control](https://mlsanity.com/paper/2104.02646/) (2021)
- [Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties](https://mlsanity.com/paper/2306.15668/) (2023)
- [Efficient Differentiable Simulation of Articulated Bodies](https://mlsanity.com/paper/2109.07719/) (2021)
- [Fine-grained differentiable physics: a yarn-level model for fabrics](https://mlsanity.com/paper/2202.00504/) (2022)
- [Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine](https://mlsanity.com/paper/2209.06261/) (2022)
- [Inferring Dynamic Physical Properties from Video Foundation Models](https://mlsanity.com/paper/2510.02311/) (2025)
- [A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data](https://mlsanity.com/paper/2203.00041/) (2022)

## Abstract (arXiv)

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 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. As a side benefit, 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.