Solving differential equations with constructed neural networks

A novel hybrid method for the solution of ordinary and partial differential equations is presented here. The method creates trial solutions in neural network form using a scheme based on grammatical evolution. The trial solutions are enhanced periodically using a local optimization procedure. The proposed method is tested on a series of ordinary differential equations, systems of ordinary differential equations as well as on partial differential equations with Dirichlet boundary conditions and the results are reported.