CTSR: Cartesian tensor-based sparse regression for data-driven discovery of high-dimensional invariant governing equations

Accurate and concise governing equations are crucial for understanding system dynamics. Recently, data-driven methods such as sparse regression have been employed to automatically uncover governing equations from data, representing a significant shift from traditional first-principles modeling. However, most existing methods focus on scalar equations, limiting their applicability to simple, low-dimensional scenarios, and failing to ensure rotation and reflection invariance without incurring significant computational cost or requiring additional prior knowledge. This paper proposes a Cartesian tensor-based sparse regression (CTSR) technique to accurately and efficiently uncover complex, high-dimensional governing equations while ensuring invariance. Evaluations on two two-dimensional (2D) and two three-dimensional (3D) test cases demonstrate that the proposed method achieves superior accuracy and efficiency compared to the conventional technique.

@article{zhang2025_2504.07618,
  title={ CTSR: Cartesian tensor-based sparse regression for data-driven discovery of high-dimensional invariant governing equations },
  author={ Boqian Zhang and Juanmian Lei and Guoyou Sun and Shuaibing Ding and Jian Guo },
  journal={arXiv preprint arXiv:2504.07618},
  year={ 2025 }
}