Awards issued prior to january 20, 2025, were funded under previous administrations and may not reflect the priorities and policies of the current administration.** as the demand for fish increases…

Awards issued prior to january 20, 2025, were funded under previous administrations and may not reflect the priorities and policies of the current administration.** as the demand for fish increases and the aquaculture systems expand to deeper oceans, traditional manual operations and monitoring are becoming less effective. This drives the urgent need to develop and deploy intelligent autonomous systems like digital twin models, which are virtual representations of real aquaculture systems. The success of the project can help make aquaculture more automated and more sustainable. To achieve this goal, we will build fast machine-learning models that can accurately present the dynamics of aquaculture systems in different environmental and operation states. The data to train the models comes from the hydrodynamics simulations of aquaculture systems. The comparison of the predictions from the machine learning model and real-time sensor measurements can guide the field operators to take immediate control actions and prevent potential system failures. We aim to release the datasets and predictive models and present the work in journals, conferences, and workshops in aquaculture engineering and machine learning. We will also create a graduate-level course on sustainable aquaculture at arizona state university. The success of the project will revolutionize the intelligent and precise operations of aquaculture systems and will prevent huge economic losses due to undesirable weather and faulty operations.