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of an artificial intelligence model; 3- Machine Learning of the AI-model. The ultimate objective is to generate an AI-solver for practical applications in container securing and stowage contexts such as onboard
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environment; Development of an artificial intelligence model; Machine Learning of the AI-model. The ultimate objective is to generate an AI-solver for practical applications in container securing and stowage
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platform by using machine learning to train models on a very large number of historical faults and weather observations, and then use these to test the resilience of energy assets using the latest UKCP18
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environment facing various disturbances, so they need to constantly adapt ML models accordingly. The research aims to develop a new generation of lifelong machine learning algorithms for processing sensor data
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electricity operators can perform climate resilience stress tests on their networks. The project will test the feasibility of producing such a platform by using machine learning to train models on a very large
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techniques for NH3/H2/air combustion in the context of Reynolds Averaged Navier-Stokes (RANS) and Large Eddy Simulations (LES) and new models will be proposed based on physical principles, and machine learning
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of Reynolds Averaged Navier-Stokes (RANS) and Large Eddy Simulations (LES) and new models will be proposed based on physical principles, and machine learning techniques trained based on DNS data. Siemens
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lifelong machine learning algorithms for processing sensor data streams on heterogeneous hardware infrastructures – devices/edge clusters/clouds – to ensure the desired system properties such as security