Numerical methods approximate solutions to fractional integro-differential equations, indicating their practicality for complex nonlinear problems.
Key Points
This research aims to compare a modified Adomian Decomposition Method and Artificial Neural Networks for solving fractional integro-differential equations.
Modified Adomian Decomposition Method is employed to enhance convergence and compute Adomian polynomials effectively.
Artificial Neural Networks are used to approximate solutions through optimized weight and bias parameters.
Various test cases are assessed to evaluate the accuracy of both methods.
Both methods demonstrate high accuracy in approximating solutions to complex nonlinear problems.
Numerical results suggest that the modified Adomian Decomposition Method is effective in improving convergence.
Artificial Neural Networks provide reliable solutions utilizing their universal approximation capabilities.