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A Taylor Based Sampling Scheme for Machine Learning in Computational Physics
Machine Learning (ML) is increasingly used to construct surrogate models for physical simulations.
Leveraging Local Variation in Data: Sampling and Weighting Schemes for Supervised Deep Learning
In the context of supervised learning of a function by a neural network, we claim and empirically verify that the neural network yields better results when the distribution of the data set focuses on regions where the function to learn is steep.
Variance Based Sample Weighting for Supervised Learning
In the context of supervised learning of a function by a Neural Network (NN), we claim and empirically justify that a NN yields better results when the distribution of the data set focuses on regions where the function to learn is steeper.
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