A neural network solver for differential equations

We propose a solver for differential equations, which only use one neural network, building of multi-layer-structure and can be learned. The learning method is defined as an equation that resembles the BR The techniques are based on the analogue type of neural network, its derivative expression and iterations which is similar to the BP algorithm. Precision of the solution depends on the learning-error of the analogue type of neural network. The structure of the solver is equivalent to the multi-layer neural network; therefore, a parallel processing can be done.