Abstract
As the global population grows, ensuring sustainable food production has become critical. Marine aquaculture provides a sustainable and scalable source of protein; however, its continued expansion requires the development of novel technologies that enable remote management and autonomous operations. Digital twin technology emerges as a transformative tool for realizing this goal, yet its adoption remains limited. Fish net cages—flexible, floating structures—are critical but vulnerable components of aquaculture systems. Exposed to harsh and dynamic marine conditions, they experience substantial hydrodynamic loads that can cause structural damage leading to fish escapes, environmental impacts, and financial losses. We propose a multifidelity surrogate modeling framework for integration into a digital twin that enables real-time monitoring of net cage structural dynamics under stochastic marine conditions. At the core of the framework lies the nonlinear autoregressive Gaussian process method, which captures complex, nonlinear cross-correlations between models of varying fidelity. It combines low-fidelity simulation data with a limited set of high-fidelity field sensor measurements, which, although accurate, are costly and spatially sparse. The framework was validated at the SINTEF ACE fish farm in Norway, where the digital twin assimilates online metocean data to accurately predict net cage displacements and mooring line loads, closely matching field measurements. This approach is especially valuable in data-scarce environments, offering rapid predictions and real-time structural representation. Beyond monitoring, the developed digital twin enables proactive assessment of structural integrity and supports remote operations with unmanned underwater vehicles. Finally, we compare Gaussian processes and graph convolutional networks for predicting net cage deformation, demonstrating the superior ability of the latter to capture in complex structural behaviors.