Construction of approximate neural network models according to heterogeneous data

Neural network approach to the robust mathematical model construction according to heterogeneous pieces of information (equations, conditions, experimental data, etc.) is considered. The case of ordinary differential equations and the case of partial differential equations and possible generalizations as well are key problems in the paper. Some model examples are given.