Hossein Hosseinzadeh

Approximation due to its inherent complexity and the vast volume of information involved. This thesis explores the use of compactly supported radial basis functions (CSRBF) as an innovative approach to effectively approximate surfaces derived from large, scattered geo data. The research begins by addressing the limitations of global radial basis functions (RBF), which often struggle to ensure continuity and smoothness in surface reconstruction. By leveraging CSRBF, which possess localized influence, this study proposes a meshless framework that enhances computational efficiency while maintaining high precision in surface approximation. Experimental results demonstrate that CSRBF with a lower degree of continuity are more suitable than other RBF methods, particularly global ones, due to the geometric conditions imposed on the data.