Symbolic identification of tensor equations in multidimensional physical fields

TL;DR

This paper introduces SITE, a novel data-driven framework for identifying tensor equations in physical fields, combining genetic programming, dimensional checks, and tensor regression to accurately recover governing relations from complex data.

Contribution

The work presents a general tensor equation identification method that extends symbolic regression to multidimensional tensor relationships, with innovations for robustness and efficiency.

Findings

Successfully recovers tensor equations from synthetic data with noise

Identifies constitutive relations from molecular simulation data

Adapts to different flow conditions, demonstrating versatility

Abstract

Recently, data-driven methods have shown great promise for discovering governing equations from simulation or experimental data. However, most existing approaches are limited to scalar equations, with few capable of identifying tensor relationships. In this work, we propose a general data-driven framework for identifying tensor equations, referred to as Symbolic Identification of Tensor Equations (SITE). The core idea of SITE--representing tensor equations using a host-plasmid structure--is inspired by the multidimensional gene expression programming (M-GEP) approach. To improve the robustness of the evolutionary process, SITE adopts a genetic information retention strategy. Moreover, SITE introduces two key innovations beyond conventional evolutionary algorithms. First, it incorporates a dimensional homogeneity check to restrict the search space and eliminate physically invalid expressions. Second, it replaces traditional linear scaling with a tensor linear regression technique, greatly enhancing the efficiency of numerical coefficient optimization. We validate SITE using two benchmark scenarios, where it accurately recovers target equations from synthetic data, showing robustness to noise and small sample sizes. Furthermore, SITE is applied to identify constitutive relations directly from molecular simulation data, which are generated without reliance on macroscopic constitutive models. It adapts to both compressible and incompressible flow conditions and successfully identifies the corresponding macroscopic forms, highlighting its potential for data-driven discovery of tensor equation.