Cascade neural networks in variational methods for boundary value problems

This paper is devoted to the case studies of function approximation capabilities of neural networks, treated as variational trial solutions of the boundary and initial value problems for the systems of partial differential equations. Two approaches are considered: common self-differentiable feedforward nets and incrementally grown cascade nets. It is also pointed out that neural networks may be useful for robust and flexible approximations of numerical solutions obtained by finite difference methods