This study investigates analytical solutions of fractional differential equations (FDEs) using the MathHandbook symbolic computation platform. The objective is to evaluate how the unified operator ds (y,x,α) and the dsolve engine handle differential equations of arbitrary order, including integer-order ODEs/PDEs, fractional ODEs and PDEs, fractional integral equations, mixed differential–integral equations, systems of fractional equations, complex-order equations, and variable-order equations. Comparative analysis shows that general solutions exhibit order-dependent exponential behavior, while particular solutions remain invariant for linear equations with identical forcing terms. These results highlight the coherence of fractional calculus and demonstrate how AI-Driven symbolic computation enhances mathematical modeling, verification, and mathematics education.
Keywords: Fractional Differential Equations; Fractional Calculus; Symbolic Computation; Mathhandbook; Analytical Solutions; Differential Equations; Fractional-Order Derivatives; Variable-Order Equations; Complex-Order Equations; AI-Driven Mathematics