The first step is to determine the principal dimensions of the design ship, such as length between perpendiculars, beam, draft and depth when accomplishing the design of a new vessel. To make this process easier, a database with a large amount of existing ship data and a regression analysis technique are needed. Recently, deep learning, a branch of artificial intelligence (AI) has been used in regression analysis. In this paper, deep learning neural networks are used for regression analysis to find the regression function between the input and output data. To find the neural network structure with the highest accuracy, the errors of neural network structures with varying the number of the layers and the nodes are compared. In this paper, Python TensorFlow Keras API and MATLAB Deep Learning Toolbox are used to build deep learning neural networks. Constructed DNN (deep neural networks) makes helpful in determining the principal dimension of the ship and saves much time in the ship design process.

As a “kind of” mature ship form, planing hull has been widely used in military and civilian areas. Therefore, a reasonable design for planing hull becomes more and more important. For planing hull, resistance and trim are always the most important problems we are concerned with. It affects the planing hull’s economic efficiency and maneuverability very seriously. Instead of the expensive towing tank experiments, the development of computer comprehensive ability allows us to previously apply computational fluid dynamics(CFD)to the ship design. In this paper, the CFD method and Goal Driven Optimization (GDO) were used in the estimations of planing hull resistance and running attitude to provide a possible method for performance computation of planing hull.