It is well known that floating structures are often used in ocean engineering. To analyze the hydrodynamic properties of floating structures, various methods, such as the Boundary Element Method and the Finite Element Method can be used. In this work we use Boundary Element Method named diffraction theory to analyze simple offshore structures. In this theory, assuming potential flow to calculate force and moments coefficients. Using a special form of green's function that can issue boundary conditions such as the free surface, sea bed and the radiation will include an important part of work. To estimate hydrodynamic coefficients we have to calculate Green function and its derivative’s. Green function is a very complex function and its calculation is very time-consuming. On the other hand one of the ways that the ability to calculate complex functions in a very short time, is use artificial neural networks. Artificial neural networks with input and output processing are able to establish a relationship between them. We utilize this ability to estimate Green function and its derivations. The ANN are trained to learn the relationships by experience so we must have a data set to learn ANN. At the first step we calculate Green function and its derivative’s by justify;In this thesis different neural networks were trained and then used these networks to calculation of force coefficients on simple geometry.We use two types of neural network namely backpropagation networks and radial basis networks. This network has six inputs and six output. Force and moment coefficients on the geometry calculated by using neural network and without using it. The results shows that if these networks properly trained can be calculate hydrodynamic coefficients with high accuracy. CPU time comparison shows that elapsed time clearly decreases when we use ANN. Keywords: Diffraction Theory, Artificial Neural Network, Wave Load, Green’s Function.
