Dimensional homogeneity constrained gene expression programming for discovering governing equations

Understand

Data-driven discovery of governing equations is of great significance for helping us understand intrinsic mechanisms and build physical models.

• Recently, numerous highly innovative algorithms have emerged, aimed at inversely discovering the underlying governing equations from data, such as sparse regression-based methods and symbolic regression-based methods.
• Along this direction, a novel dimensional homogeneity constrained gene expression programming (DHC-GEP) method is proposed in this work.
• DHC-GEP simultaneously discovers the forms and coefficients of functions using basic mathematical operators and physical variables, without requiring pre-assumed candidate functions.