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On minimizing the training set fill distance in machine learning regression
We empirically show that selecting a training set by aiming to minimize the fill distance, thereby minimizing our derived bound, significantly reduces the maximum prediction error of various regression models, outperforming alternative sampling approaches by a large margin.
On the Interplay of Subset Selection and Informed Graph Neural Networks
However, learning on large datasets is strongly limited by the availability of computational resources and can be infeasible in some scenarios.
Multi-resolution Dynamic Mode Decomposition for Damage Detection in Wind Turbine Gearboxes
We introduce an approach for damage detection in gearboxes based on the analysis of sensor data with the multi-resolution dynamic mode decomposition (mrDMD).
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