Physics-constrained symbolic regression for discovering closed-form equations of multimodal water retention curves from experimental data

Researchers developed a physics-constrained machine learning framework that uses genetic programming to automatically discover closed-form mathematical equations for modeling water retention in porous materials with complex pore structures. The approach represents mathematical expressions as binary trees and incorporates physical constraints to ensure scientifically valid solutions.