Function Decomposition Tree

Function decomposition trees represent complex multivariate functions as hierarchical structures of simpler functions, aiming to reveal underlying relationships and improve interpretability. Current research focuses on adapting these trees for various applications, including modeling spatiotemporal changes in complex 3D structures (like plant growth) using techniques like Riemannian geometry and optimizing their construction within machine learning contexts (e.g., genetic programming) to enhance model efficiency and generalization. This approach offers a powerful tool for knowledge representation and analysis across diverse scientific domains, facilitating both understanding of complex systems and the development of more efficient and explainable models.