-
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
-
al., “Deep Transfer Learning for Fault Diagnosis”, IEEE Transactions on Industrial Electronics, 2020. • Zhang C. et al., “Graph Neural Networks for Power Systems”, Electric Power Systems Research, 2023
-
neural stem cell (NSC) niche. Recent studies have revealed key aspects of human rhombic lip (hRL) organization and shown that defects in its development underlie several human syndromes. These findings
-
, particularly Graph Neural Networks (GNNs), which show great promise. These methods have already demonstrated performance at least comparable to current Track Finding algorithms, with significant room for further