## 1 Introduction  
Recently, there has been a growing inclination across various disciplines towards data science given the advances in various fields, such as voice-to-text recognition [1], natural language processing (NLP) [2], and image processing [3]; This trend is evident not only in industry but also in everyday daily activities, like in mobile phone applications. The fervent interests stem from the capability of the proposed models in replicating real-world phenomena through accurate mapping of input-output, especially for complicated tasks that could have hardly been done by analytical formula. Constructing a linear mapping of input-output relationships is straightforward, but the challenge arises from inherent nonlinear behaviors. ML models effectively capture these nonlinearities through iterative training on a dense network of linear models' accumulation. The workflow is simple yet computationally intensive. Nonlinear datasets are divided into large arrays of linear models based on Equation (1), which involves vector of weight (w) corresponding to slope and vector of bias (b) factor to intercept. These factors are adjusted iteratively through forward/backpropagation to minimize the difference with loss function (L), governing the mapping between input x and output F(x) data as per Equation (2) [4]. \[\mathrm{L-F(x)}=\mathrm{min}\sum_{i=1}^{n}w_{i}x_{i}+\mathrm{b_{i}} \tag{1}\]  
\[w_{i}=\mathrm{w_{i-1}}-\frac{\partial\left(\mathrm{Loss\ Function-prediction} \right)}{\partial w_{i-1}}\]  
\[b_{i}=\mathrm{b_{i-1}}-\frac{\partial\left(\mathrm{Loss\ Function-prediction} \right)}{\partial b_{i-1}} \tag{2}\]  
Inspired by other disciplines, the current wave has emerged in maritime and ocean engineering studies. Despite the excitement resulting from the new trend, the research has fallen short in real-world scenarios due to many limitations and challenges. First, the machine and deep learning models are efficient with deterministic inputs, whereas the environmental forces at sea are intrinsically stochastic [5]. As a result, a trained model with deterministic data would not work efficiently for real-world data since the deterministic input cannot mimic the involved, entwined relationship and chaos within stochastic inputs [6]. Theoretically, replicating the stochastic input-output is possible through probabilistic approaches; however, the associated computational costs can be inordinately expensive. On the other hand, using raw real-world data as input to the ML model comes with other challenges. Fundamentally, real-world inputs are rife with complex interactions of dynamic factors like waves and winds, posing challenges in distinguishing dominant forces and attributing a share of them to the corresponding system response (output) [7]. Also, given the unstructured nature of environmental conditions, i.e., waves and winds, constructing a deterministic input-output relationship is infeasible due to the lack of control over input and measurement inaccuracy and errors [8]. As a result, uncertainties, including unknown causes, permeate the inputs, and the associated phenomena, like viscous boundary layers, cannot be completely understood, making it difficult to quantify uncertainties. Additionally, in contrast to fields like automated driving, which predominantly deal with structured data [5], maritime data exhibits diverse forms with different fidelity levels, including field data recordings, model-scaled experiments, computational fluid dynamics (CFD), and analytical simulations relying on linear models and empirical parameters. Previous research has shown that a model trained with a specific fidelity level cannot be effectively generalized to other fidelity levels [6; 7]. For example, a model trained in simulation data struggles to predict outcomes based on model-scale data and vice versa.