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The hydromechanics analysis of floating offshore wind turbines is a fundamental and time consuming part of the design process, traditionally analysed with methods of computational fluid dynamics. In this work, an alternative computational framework is suggested, able to significantly accelerate the design process with minimal accuracy loss. Through the use of a state-ofthe-art potential-flow code, a surrogate model is developed with the aim to approximate the Response Amplitude Operators of any arbitrary floating offshore wind turbine of the spar buoy type. The results, measured in terms of accuracy and computational effort, demonstrate that this approach is able to approximate the potential-flow solver with very high accuracy at a fraction of the computational cost.

Answer Set Programming (ASP) is a well-established declarative AI formalism for knowledge representation and reasoning. ASP systems were successfully applied to both industrial and academic problems. Nonetheless, their performance can be improved by embedding domainspecific heuristics into their solving process. However, the development of domain-specific heuristics often requires both a deep knowledge of the domain at hand and a good understanding of the fundamental working principles of the ASP solvers. In this paper, we investigate the use of deep learning techniques to automatically generate domain-specific heuristics for ASP solvers targeting the well-known graph coloring problem. Empirical results show that the idea is promising: the performance of the ASP solver wasp can be improved.

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