  As the global population grows and climate change intensifies, sustainable
food production is critical. Marine aquaculture offers a viable solution,
providing a sustainable protein source. However, the industry's expansion
requires novel technologies for remote management and autonomous operations.
Digital twin technology can advance the aquaculture industry, but its adoption
has been limited. Fish net cages, which are flexible floating structures, are
critical yet vulnerable components of aquaculture farms. Exposed to harsh and
dynamic marine environments, the cages experience significant loads and risk
damage, leading to fish escapes, environmental impacts, and financial losses.
We propose a multifidelity surrogate modeling framework for integration into a
digital twin for real-time monitoring of aquaculture net cage structural
dynamics under stochastic marine conditions. Central to this framework is the
nonlinear autoregressive Gaussian process method, which learns complex,
nonlinear cross-correlations between models of varying fidelity. It combines
low-fidelity simulation data with a small set of high-fidelity field sensor
measurements, which offer the real dynamics but are costly and spatially
sparse. Validated at the SINTEF ACE fish farm in Norway, our digital twin
receives online metocean data and accurately predicts net cage displacements
and mooring line loads, aligning closely with field measurements. The proposed
framework is beneficial where application-specific data are scarce, offering
rapid predictions and real-time system representation. The developed digital
twin prevents potential damages by assessing structural integrity and
facilitates remote operations with unmanned underwater vehicles. Our work also
compares GP and GCNs for predicting net cage deformation, highlighting the
latter's effectiveness in complex structural applications.
