(untitled) (Patan Prospective Journal, 2024)
This paper explores the mathematical foundations and applications of higher-order derivatives across various domains, including tensor calculus, control system optimization, and machine learning. Higher-order derivatives, extending beyond the first derivative, play a crucial role in the analysis of complex systems. They are used in determining system stability, sensitivity analysis, and optimization in control theory, where they enhance the robustness and accuracy of control mechanisms. In machine learning, second-order optimization methods leverage higher-order derivatives to improve convergence rates in neural networks and deep learning models. In tensor calculus and differential geometry, higher-order derivatives provide insights into curvature and torsion, essential for understanding the behavior of manifolds in fields such as general relativity. The paper discusses the use of symbolic computation tools for automating the calculation of higher order derivatives, and spectral and finite difference methods for solving partial differential equations (PDEs) using higher-order terms. Applications, derivations, and numerical examples are presented to highlight the impact of higher-order derivatives in these domains.