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predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
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, electrochemical storage, energy conversion, and controllable loads. • Generation of normal, disturbed, and degraded operating scenarios taking into account different network configurations. • Integration
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