Machine Learning Based Computational Models for Increased Accuracy and Enabling Digital Twins

Abstract Computational simulation approaches have long been a cornerstone of design in marine and offshore structures. More recently, they have also become essential for optimizing operations and ensuring sustainability. The digital twin (DT) framework has enabled to bridge the gap between simulation and its practical application in controlling, monitoring, and operating marine assets. However, the applications where complex non-linear problems are solved in real or close to real-time are still limited. It is proposed that this could be overcome by the recent rise of artificial intelligence (AI) and machine learning methods, which are promising technologies that can make current computational models more versatile. Namely, data-driven surrogate models or neural networks embedded in numerical algorithms enable the relegation of computationally complex calculations into simple, functional forms, opening a pathway to a new generation of digital twins. Memory-augmented neural networks offer a viable solution for applications involving memory-dependent problems, such as ship motion control or material fracture. These neural network models operate incrementally, much the same as the computational models they are inserted to. This work explores a critical yet underexamined aspect of these incremental machine learning models that is important when considering the pathway to real-time digital twins for ship motion control or non-linear structural analysis.