Understanding nonlinearities in in particulate materials using kriging-augmented gene expression programming

Key Points

• Nonlinearities in particulate materials impact predictive modeling, leading to varying outcomes with distinct characteristics.
• The study discovered that kriging-augmented gene expression programming enhances data interpolation and modeling precision.
• Analysis involved advanced predictive modeling techniques to better understand the behavior of particulate materials under different conditions.
• Findings highlight the necessity for improved algorithms in the exploration of complex material behaviors.