Present operational constraints require that the computational effort for the estimation of S nl should be of the same order of magnitude as the remainder of the wave model. The DIA tends to unrealistically increase the directional width of spectra, has a systematic spurious impact on the shape of the spectrum near the spectral peak frequency, and has a much too strong signature at high frequencies. Although this approach gives good results, it is counterproductive, because it prohibits development of dissipation source terms based on solid physical considerations. With our increased understanding of other source terms, this becomes a bigger obstacle for the further development of third-generation wave models. Considering the above, it is of crucial importance for the development of third generation wave models to develop a cheap yet accurate approximation for S nl .
Neural networks consist of layers of uniform processing elements, nodes, units, or neurons.
The neurons and layers are connected according to a specific architecture or topology. A so-called multilayer perceptron is a simple architecture which is sufficient for any continuous nonlinear input to output mapping. A multilayer perceptron always has at least one hidden layer with k neurons. NN solutions for different problems will differ in several important ways. For each particular problem, n and m are determined by the dimensions of the input and output vectors X and Y .