## 1 _Application_
Those numerical analysis methods solve a version of the Navier-Stokes to compute the wave-induced loads, hydrodynamic characteristics, and response of WEC devices subjected to unidirectional regular waves [1; 10; 11; 12; 13]. The CFD methods use comprehensive numerical schemes to solve the entire system of mathematical equations and demonstrate three advantages in comparison with the physical modelling: they are more time and cost effective, and they have no scaling limitations. Nonetheless, the validation of the CFD-based codes require experimental datasets [3; 6; 14]. However, the main disadvantage of the CFD technique is that it still takes too much time and expertise to obtain satisfactory results, in particular, with irregular wave cases. The use of CFD-based methods for practical WEC applications is limited to the final stages of design. To address those shortcomings, various simplified numerical models have been proposed based on potential flow models [15]. However, although they perform faster than CFD-based codes, they may produce inaccurate predictions since they ignore viscosity effects, air entrapment phenomenon, and the influence of higher order waves. For instance, using potential-flow-based techniques [16], the hydrodynamic efficiency of an OWC device is often over-predicted [17]. Nowadays, the use of CFD methods for practical WEC applications is limited to the final stages of design, if at all. The grey box methods are not very popular for the OWC design. The only emerging exception is a method based on the adaptive neuro-fuzzy inference system (ANFIS) which have been applied to different types of WEC devices [18; 19]. It combines a multilayer artificial neural network (ANN) with fuzzy logic. The latter allows us to apply \"if-then\" rules with linguistic terms rather than rules with crisp numerical values [20]. Fuzzy logic is useful in problems containing inaccuracy, where the propositions have a degree of membership (between 0 and 1) to the set of the true propositions as in [21; 22]. ANFIS usesfuzzy and partial knowledge (coded in the fuzzy \"if-then\" rules) about the modelled physical process. The outcome of ANFIS can be partially explained and is easier to interpret than the prediction methods. On the negative side, ANFIS does require dataset for training the model construction. The greatest shortcoming of the approach is that its effectiveness is based on comprehensive expert knowledge about WEC systems. Such sets of \"if-then\" rules are problem specific and are expensive and time-consuming to acquire. The black box (BB) methods only aim to approximate the measured output for different inputs based on an information in a known dataset. No knowledge about the modelled physical process is used. There are numerous types of BB methods, but only two types are relevant for the WEC design: ANN and multiple linear regression (MLR). ANN is a supervised predictive method capable universally completely to approximate any multi-dimensional output function of continuous and discrete input variables, as proven in a theorem [23]. It has proved to be efficient in modelling complex engineering relationships encoded in datasets [24] and were applied to numerous coastal engineering problems [25], including to WEC applications [10]. The advantages of ANN in comparison with the other methods are that they are cheap, fast to predict and easy to realize due to their independence from the modelled process [26]. On the negative side, the complexity of the modelling function often prevents extraction of relevant information suitable to identify the analytical function derived by the ANN. The complex structure of ANN is determined by the so-called hyper-parameters (e.g., the number of hidden layers, the number of neurons in each hidden layer, etc. ), which are determined using a validation set. Therefore, the available data set in ANN has to be partitioned into three parts: training, validation and testing part. This additionally complicates the construction of a reliable ANN model when the available data set is not large.