Solution of gas-dynamics problems containing different fine structures (shock waves, boundary layers, traces, jets, etc.) as well as areas within which the flows are described by different models of turbulence by traditional grid methods is quiet difficult. These tasks are encouraged to address, using neural network technology in the analysis and grid-analytical form by stochastic optimization. The proposed method is demonstrated on the problems of mixing jets. Numerical experiments have shown several advantages of this approach: reducing the cost of computer time, good accuracy, the connection of patchy pattern of flow in a single unit.